A HADES and CARMENES collaboration - arxiv.org · A&A proofs: manuscript no. Gl49b_ﬁnal...

Astronomy & Astrophysics manuscript no. Gl49b_final c©ESO 2019March 26, 2019

Gliese 49: Activity evolution and detection of a super-Earth?

A HADES and CARMENES collaboration

M. Perger1, 2, G. Scandariato3, I. Ribas1, 2, J. C. Morales1, 2, L. Affer4, M. Azzaro5, P. J. Amado6,G. Anglada-Escudé6, 7, D. Baroch1, 2, D. Barrado8, F. F. Bauer6, V. J. S. Béjar9, 10, J. A. Caballero8,

M. Cortés-Contreras8, M. Damasso11, S. Dreizler12, L. González-Cuesta9, 10, J. I. González Hernández9, 10,E. W. Guenther13, T. Henning14, E. Herrero1, 2, S.V. Jeffers12, A. Kaminski15, M. Kürster14, M. Lafarga1, 2, G. Leto3,

M. J. López-González6, J. Maldonado4, G. Micela4, D. Montes16, M. Pinamonti11, A. Quirrenbach15, R. Rebolo9, 10, 17,A. Reiners12, E. Rodríguez6, C. Rodríguez-López6, J. H. M. M. Schmitt18, A. Sozzetti11, A. Suárez Mascareño9, 19,

B. Toledo-Padrón9, 10, R. Zanmar Sánchez3, M. R. Zapatero Osorio20, and M. Zechmeister12

(Affiliations can be found after the references)

Accepted: 11 March 2019

ABSTRACT

Context. Small planets around low-mass stars often show orbital periods in a range that corresponds to the temperate zones of theirhost stars which are therefore of prime interest for planet searches. Surface phenomena such as spots and faculae create periodicsignals in radial velocities and in observational activity tracers in the same range, so they can mimic or hide true planetary signals.Aims. We aim to detect Doppler signals corresponding to planetary companions, determine their most probable orbital configurations,and understand the stellar activity and its impact on different datasets.Methods. We analyzed 22 years of data of the M1.5 V-type star Gl 49 (BD+61 195) including HARPS-N and CARMENES spec-trographs, complemented by APT2 and SNO photometry. Activity indices are calculated from the observed spectra, and all datasetsare analyzed with periodograms and noise models. We investigated how the variation of stellar activity imprints on our datasets. Wefurther tested the origin of the signals and investigate phase shifts between the different sets. To search for the best-fit model wemaximize the likelihood function in a Markov chain Monte Carlo approach.Results. As a result of this study, we are able to detect the super-Earth Gl 49b with a minimum mass of 5.6 M⊕. It orbits its hoststar with a period of 13.85 d at a semi-major axis of 0.090 au and we calculate an equilibrium temperature of 350 K and a transitprobability of 2.0 %. The contribution from the spot-dominated host star to the different datasets is complex, and includes signalsfrom the stellar rotation at 18.86 d, evolutionary timescales of activity phenomena at 40–80 d, and a long-term variation of at leastfour years.

Key words. planetary systems – techniques: radial velocities – stars: late-type – stars: activity – stars: individual: Gl 49 – methods:data analysis

1. Introduction

Time-series observations with high-resolution spectrographssuch as the High Accuracy Radial velocity Planet Searcher of thesouthern (HARPS; Mayor et al. 2003) and northern (HARPS-N; Cosentino et al. 2012) hemispheres, the Calar Alto high-Resolution search for M dwarfs with Exoearths with Near-infrared and optical echelle Spectrographs (CARMENES; Quir-renbach et al. 2018), or the iodine-cell HIgh Resolution echelleSpectrograph (HIRES; Vogt et al. 1994) are used to detect andconfirm planetary companions of stars by the Doppler shifts oftheir spectra. The measured radial velocities (RVs) show vari-ations over time that are induced by the Keplerian orbit of the

Send offprint requests to: Manuel Perger, e-mail: [email protected]? Based on observations made with the Italian TNG, operated on the

island of La Palma, Spain; the CARMENES instrument installed atthe 3.5 m telescope of the Calar Alto Observatory, Spain; the roboticAPT2 located at Serra La Nave on Mt. Etna, Italy; and the T90 tele-scope at Sierra Nevada Observatory, Spain; Table A.1 is only availablein electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr(130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/.

planet, or planets, and a contribution of the stellar surface. Thelatter can include actual motions on the surface such as oscilla-tions or surface granulation with different short-term timescales(Dumusque et al. 2011), but also phenomena such as dark spotsand bright plages, which introduce RV variations by reducingthe number of photons from either the blue- or the red-shiftedside of the rotating host star. Those phenomena are caused bythe magnetic field of the star and its detailed structure and vari-ability. The typical lifetime of those surface phenomena followsa parabolic decay law depending on size (Petrovay & van Driel-Gesztelyi 1997) and can last for up to several rotation periods(Bradshaw & Hartigan 2014; Scandariato et al. 2017) or evenlonger (see, e.g., Davenport et al. 2015). In the Sun, we also ob-serve a long-term magnetic cycle of approximately 11 years1 thatis induced by the migration of the spot patterns across differentlatitudes and by the variation of the spot number. Such cyclesare expected for all stellar types (Lovis et al. 2011; Suárez Mas-careño et al. 2016). The stellar contribution to the RVs, therefore,can be described partly as uncorrelated noise, but also possibly

1 22 years if considering polarity

Article number, page 1 of 23

arX

iv:1

903.

0480

8v2

[as

tro-

ph.E

P] 2

5 M

ar 2

019

http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/

A&A proofs: manuscript no. Gl49b_final

correlated with the rotational period of the star, the lifetime ofthe activity phenomena, or any long-term magnetic cycle.

The disentangling of activity-induced variations and plane-tary signals is of great interest, especially due to the numerousplanet searches around M dwarfs that are currently being con-ducted. The rotational periods of these cool stars range fromsome days to months and coincide with the orbital periods ofplanets in hot or temperate orbits which are, therefore, prime tar-gets for detection and characterization (Kiraga & Stepien 2007;Newton et al. 2016; Díez Alonso et al. 2019). Planets of a givenmass and period induce larger RV amplitudes in low-mass starsbut these stars are also magnetically more active (e.g., Testa et al.2015), with longer-living surface phenomena (Giles et al. 2017)and less influence of surface granulation than stars of earliertype (Berdiñas et al. 2016). They are the most numerous stars inthe Galaxy and in the immediate solar vicinity (75% of all starscloser than 10 pc are M dwarfs according to the RECONS sur-vey2), giving us the possibility to probe our neighboring plane-tary population. Most importantly, the surveys around M dwarfsare the best possibility to date to access the domain of rockyplanets in order to study their atmospheres and to apply first sta-tistical calculations (e.g., Mayor et al. 2011; Dressing & Char-bonneau 2013; Bonfils et al. 2013; Perger et al. 2017a).

Such surveys include the HARPS-N red Dwarf ExoplanetSurvey (HADES; Affer et al. 2016), which monitored regularlyaround 80 M0- to M3-type stars and detected and confirmedseven Neptune- to Earth-like planets (Suárez Mascareño et al.2017a; Perger et al. 2017b; Pinamonti et al. 2018; Affer et al.2019). The HADES program is a collaboration between Italianand Spanish institutes, and has also extensively explored the ro-tational and magnetic behavior of those low-mass stars (Mal-donado et al. 2017; Scandariato et al. 2017; Suárez Mascareñoet al. 2018; González-Álvarez et al. 2019). Another survey isconducted with the CARMENES instrument, which is moni-toring regularly more than 300 M-type dwarfs selected fromthe input catalog Carmencita (Caballero et al. 2016a). The pro-gram has already confirmed a number of planets (Trifonov et al.2018; Sarkis et al. 2018) and detected up to seven previouslyunknown planets (Reiners et al. 2018a; Kaminski et al. 2018;Luque et al. 2018; Nagel et al. 2019), including a cold compan-ion of the nearby Barnard’s Star (Ribas et al. 2018). The instru-ment is an effort of German and Spanish institutes to fully ex-plore for the first time a large and complete sample of nearbyM dwarfs of all spectral subtypes. With its wide range of wave-lengths, CARMENES is especially capable of disentangling dif-ferent sources of RV variability. Whereas a planetary orbit shiftsthe stellar light of all wavelengths equally, the contribution fromsurface phenomena is connected to physical processes that usu-ally depend on wavelength.

In this work we have combined observations from both theHADES and CARMENES programs in order to search for plan-etary companions around Gl 49. This is a low-mass star withspectral type M1.5 V located in the Cassiopeia constellation. Itskinematics and large proper motions suggest a membership tothe young disk population of the Milky Way (Cortés-Contreras2016; Maldonado et al. 2017). The star shows a moderate activ-ity level and Sun-like metallicity, and was found to rotate witha period of 18.4±0.7 d by HARPS-N activity indices (SuárezMascareño et al. 2018) and 19.9±0.4 d by MEarth photometry(Díez Alonso et al. 2019). We provide an overview of its ba-sic properties in Table 1. Gl 49 forms the wide binary systemWNO 51 (Washington Double Star catalog; Mason et al. 2001)

2 www.recons.org

Table 1. Basic parameters of Gliese 49 (BD+61 195, KarmJ01026+623).

Parameter Value Referenceα (J2000) 01h 02m 40.5s (1)δ (J2000) +62◦ 20’ 44” (1)Sp. type M1.5 V (2,3)d 9.856±0.003 pc (1)M 0.515±0.019 M� (4)R 0.511±0.018 R� (4)Teff 3805±51 K (4)Lbol 0.04938±0.00090 L� (4)log g 4.69±0.07 dex (4)[Fe/H] 0.13±0.16 (4)log R′HK −4.83±0.03 dex (5)pEW(Hα) −0.044±0.087 Å (6)log LX/Lbol −4.70±0.09 dex (7)V 9.56±0.02 mag (8)G 8.66 mag (1)J 6.230±0.021 mag (9)µα cos δ +731.134±0.041 mas a−1 (1)µδ +90.690±0.048 mas a−1 (1)Vr -5.777±0.066 km s−1 (10)dv/dt 12.304±0.004 cm s−1a−1 (1)Prot 18.4±0.7 d, 19.9±0.4 d (5,11)v sin i <2 km s−1 (12)HZ 0.18-0.49 au, 39-172 d (13)

Notes. (1) Gaia Collaboration et al. (2018) , (2) Alonso-Floriano et al.(2015) , (3) Maldonado et al. (2017) , (4) Schweitzer et al. (2019), (5) Suárez Mascareño et al. (2018) , (6) Schöfer et al. (2019) ,(7) González-Álvarez et al. (2019) , (8) Høg et al. (1998) , (9) Skrutskieet al. (2006) , (10) this work , (11) Díez Alonso et al. (2019) , (12) Rein-ers et al. (2018b) , (13) recent Venus/early Mars habitable zones (HZ)following Kopparapu et al. (2013).

with a fainter proper-motion companion at 293.1 arcsec to theeast, namely Gl 51 (V388 Cas, Karm J01033+623). The com-panion is a flaring M5.0 V star with a relatively large amplitudeof RV variations due to activity (Alonso-Floriano et al. 2015;Tal-Or et al. 2018; Jeffers et al. 2018), but for which reliablemembership in young moving groups, and thus an age estimate,has not been reported (e.g., Gagné et al. 2015).

In Sect. 2 we present the spectroscopic observations of Gl 49and their treatment. In Sect. 3 we analyze the variation and evolu-tion of the activity of the star with additional photometric obser-vations and various time-series data derived from the observedspectra. We study their properties by investigating their period-icities, calculating phase shifts and model the data as correlatednoise in a Gaussian process (GP) framework. We describe fur-ther in Sect. 4 how we apply different state-of-the-art models toour system in order to ascertain the existence of a planet, andto find the most likely parameters for the proposed system. Adetailed discussion about our findings regarding the discoveredsystem and the evolution of the stellar activity over the last sixyears is given in Sect. 5. We conclude the work in Sect. 6.

2. Spectroscopic observations

We obtained 137 RVs from optical spectra of the HADES pro-gram. They were observed over six seasons (S1 to S6) between3 Sep 2012 and 11 Oct 2017 with HARPS-N. The instrument isinstalled since 2012 at the 3.58 m Telescopio Nazionale Galileo(TNG) located at the Roque de Los Muchachos Observatoryin La Palma, Spain, and is connected to its Nasmyth B focus


www.recons.org

M. Perger et al.: Gliese 49: Activity evolution and detection of a super-Earth

through a front-end unit. It is a fiber-fed, cross-dispersed echellespectrograph with a spectral resolution of 115 000, covering awavelength range from 3 830 to 6 900 Å. In the HADES frame-work, we observed all targets with fixed integration times of900 s to obtain data of sufficient signal-to-noise ratio and to av-erage over short-term variations. The data were reduced withthe data reduction pipeline DRS (Lovis & Pepe 2007). We ex-tracted RVs using the Java-based Template-Enhanced Radial ve-locity Re-analysis Application code (TERRA; Anglada-Escudé& Butler 2012), which has been shown to deliver RV time-series of lower dispersion than the binary mask technique usedby the DRS, at least in the case of early M-type stars (Pergeret al. 2017a). As outlined by those authors, we added quadrati-cally a value of 1.0 m s−1 to the RV uncertainty if no correctionfor RV drifts was applied by observing simultaneously a Th-Arlamp with the second fiber. The DRS pipeline delivers a valuefor the absolute RV shift for Gl 49 of -5.777±0.066 km s−1. Thestatistics of the datasets are shown in Table 2, and the RV val-ues are given in Table A.1 and visualized in the top panels ofFig. 1. The whole HARPS-N set shows small RV uncertaintiesbut a strong variation in RV scatter over the seasons. Assuming asemi-periodic behavior, we would set a lower limit of recurringapparent RV amplitudes at approximately 1500 d, with a mini-mum at S3 to S4. Additionally, S1 shows an exceptionally largeRV scatter, pointing toward a rather variable stellar contribution.

Furthermore, we obtained spectroscopic observations withthe CARMENES instrument, installed since 2015 at the 3.51 mtelescope of the Calar Alto Observatory in Spain. Its wavelengthcoverage ranges from 5 200 to 9 600 Å in the optical and up to17 100 Å in the near-infrared, with resolutions of 94 600 and80 400, respectively (Quirrenbach et al. 2016, 2018). The visualchannel is capable of reaching RV precisions of 1 to 2 m s−1

(e.g., Trifonov et al. 2018). As reduction pipeline, we used theCARMENES Reduction And CALibration software (CARA-CAL; Caballero et al. 2016b), and to calculate the RVs, thetemplate-based SpEctrum Radial Velocity AnaLyser (SERVAL;Zechmeister et al. 2018). In the framework of the CARMENESGuaranteed Time Observation program, the target was observed80 times from 7 Jan 2016 to 25 Feb 2018, covering the three sea-sons S4, S5, and S6 of the HARPS-N observations. The opticalechelle spectra were obtained with exposure times from 100 to800 s in order to reach a signal-to-noise ratio of 150. The opticaldata set shows a low RV scatter. The data of the near-infraredchannel of CARMENES, on the other hand, are not used in thisstudy since the RV amplitudes that we are studying for our earlyM-dwarf target are expected to be smaller than the RV precision.

Gl 49 was also observed with the HIRES instrument, in-stalled since the late 1990s at the Keck I telescope located inHawaii, USA (Vogt et al. 1994). The instrument observes from3 000 to 10 000 Å with a resolution of up to 85 000 and a pre-cision of down to 1 m s−1. HIRES uses a iodine cell to monitorand correct for the RV drifts introduced by temperature changes.In total, data were taken at 21 epochs between 6 Aug 1996 and17 Oct 2011 with, on average, one observation every 259 d. Thedata were released by Butler et al. (2017), but we used the cor-rections applied by Tal-Or et al. (2019). We refer to this set ofdata in the following as season S0, since it does not overlap withour HARPS-N and CARMENES observations and since it is notvisibly separated into the seasons of observability. The data showsmall RV scatter and uncertainty. We considered for the RVs ofall three instruments instrumental RV drifts, a barycentric cor-rection following Wright & Eastman (2014), and the small sec-ular acceleration of about 0.12 m s−1yr−1 of Gl 49 (see Table 1).

3. Stellar activity

The stellar contribution to RV variations can be very complexand mimic or mask a planetary companion. In order to under-stand those effects more profoundly and to be able to includethis knowledge in our RV data modeling, we analyzed the ac-tivity level of Gl 49 and collected and derived various additionaltime series, which are generally understood as tracers for stellaractivity effects of the star and on its surface.

3.1. Activity level

Gl 49 shows a moderate activity level, which is expressedby its calcium and Hα indices as shown in Table 1 withlog R′HK=−4.8 dex and pEW(Hα)=−0.04 Å. In the recent RASS(ROSAT All-Sky Survey) source catalog by Boller et al. (2016),the source 2RXS J010318.3+622140 is close to the position ofthe companion Gl 51. Its count rate of 0.24 ct s−1 leads to an X-ray luminosity of about 1.7×1028 erg s−1 for the whole system.This value refers predominantly to Gl 51, however, a close in-spection of the images shows an extended source with an elonga-tion toward Gl 49. The observation of Gl 51 (obs-id 0742230501)in the XMM-Newton archive contains Gl 49 in its field-of-view.The two sources are clearly separated and the X-ray flux andluminosity can be determined separately. For Gl 49, González-Álvarez et al. (2019) estimate an X-ray luminosity of about3.9×1027 erg s−1 (see Table 1), which accounts for about 20%of the total flux, essentially consistent with the RASS measure-ments. We then find Gl 49 located in the upper quartile of theearly M-dwarf X-ray luminosity distribution function (Schmittet al. 1995).

3.2. Photometric data

We obtained photometric observations in the framework of theHADES and CARMENES consortia. The basic properties andthe temporal distributions of the data are shown in Table 3 and inFig. A.1.

Simultaneously to the spectroscopic observations during sea-sons S5 and S6, we collected data within the EXORAP (EX-Oplanetary systems Robotic APT2 Photometry) program. It iscarried out at INAF-Catania Astrophysical Observatory with an80-cm f/8 Ritchey-Chrétien robotic telescope (Automated Pho-toelectric Telescope, APT2) located at Serra la Nave on Mt. Etna.BVRI photometry of the star was collected over 157 nights be-tween 7 Jun 2016 and 18 Oct 2017. It covers 499 d with an av-erage of one observation every 2.0 to 2.3 d for BVRI filters. Toobtain differential photometry, we started with an ensemble ofabout ten stars, the brightest that were close to Gl 49. We checkedthe variability of each of them by building their differential lightcurves using the rest of the sample as a reference. In that way, weselected the four least variable stars of the sample for filters B, V ,and I (five stars in R). The average rms of the ensemble stars was15, 15, 24, 21 mmag in the B, V , R, and I filters, respectively.We obtained 250, 239, 230, and 223 data points, respectively,and rejected very few of them by a 5σ clip. We calculated av-erage photometric uncertainties as sky plus Poisson noise. Asvisible in Fig. A.1, Gl 49 is slightly fainter in S5 than in S6 forEXORAP filter B, but the relationship seems to shift going tolonger wavelengths until the star is brighter in S5 for filter I.

We also used the 90 cm Ritchey-Chrétien T90 telescope atSierra Nevada Observatory (SNO), Spain. It is equipped with a2k×2k CCD camera and a field of view of 13.2×13.2 arcmin2

(Rodríguez et al. 2010). The observations were collected in


http://arxiv.org/abs/obs-id/0742230


Table 2. Basic statistics and main periodicities of the different RV datasets.

Data set Nobs rms RV [m s−1] dRV [m s−1] ∆T [d] δt [d] Main periodicities [d]

HIRES S0 21 4.99 1.31 5185 259.3 17.3HARPS-N S1-S6 137 6.27 1.18 1864 13.1 19.1, 18.9, 9.6, 13.4, (18.3)

HARPS-N S1 27 9.99 1.35 151 5.8 18.6, 7.6, 14.5HARPS-N S2-S6 110 4.94 1.14 1531 14.1 9.4, 19.0, 14.4, (18.9)

CARMENES S4-S6 80 4.97 1.78 780 10.0 9.3, 18.9, (14.4)

Notes. dRV refers to the RV uncertainty, ∆T is the total observational timespan, and δt is the mean separation between different epochs. Pe-riodicities are sorted by their occurence in the prewhitening process. After the significant periods (FAP<0.1%), we show the tentative signals(0.1%<FAP<1%) in parentheses.

0 1000 2000 3000 4000 5000 6000BJD [d] - 2450000

20

10

0

10

20

RV [m

s1 ]

S0

HIRES

0.000 0.025 0.050 0.075 0.100 0.125 0.1500.00

0.25

0.50

0.75

Powe

r

0.000 0.025 0.050 0.075 0.100 0.125 0.150Frequency [days 1]

0.0

0.2

0.4

0.6

Powe

r

WF

6000 6500 7000 7500 8000BJD [d] - 2450000

20

10

0

10

20S1 S2 S3 S4 S5 S6

HARPS-N

0.000 0.025 0.050 0.075 0.100 0.125 0.1500.0

0.1

0.2

0.000 0.025 0.050 0.075 0.100 0.125 0.150Frequency [days 1]

0.0

0.5

1.0

WF

7400 7600 7800 8000 8200BJD [d] - 2450000

20

10

0

10

20S4 S5 S6

CARMENES

0.000 0.025 0.050 0.075 0.100 0.125 0.1500.0

0.1

0.2

0.3 0.1%1%

10%

0.000 0.025 0.050 0.075 0.100 0.125 0.150Frequency [days 1]

0.0

0.5

1.0

WF

Fig. 1. RV time series (top panels), GLS periodograms (middle panels), and window functions (bottom panels) of the HIRES (left column),HARPS-N (middle column) and CARMENES (right column) RV data. The different observational seasons are labeled from S0 to S6, and theunusual season S1 is colored in blue. In the periodograms, we mark the periods at 9.37 and 18.86 d with orange vertical lines, the period at 13.85 dwith a red vertical line, and the 0.1, 1, and 10 % analytical FAP levels by red horizontal dashed lines. The blue vertical dashed line for the HIRESdata shows the Nyquist frequency at (159 d)−1=0.00629 d−1.

Johnson V and R filters on 44 nights in 2018 during the periodfrom 11 Jun to 21 Sep. The observations were carried out af-ter the RV campaigns in order to search for a possible plane-tary transit. The measurements were obtained by the method ofsynthetic aperture photometry using no binning and in average25 observations of 10 to 25 s per night. Each CCD frame wascorrected in a standard way for bias and flat-fielding. Differentaperture sizes were also tested in order to choose the best onefor our observations (16 pixels). A number of carefully selectednearby and relatively bright stars within the frames were used asreference stars. Outlier points due to bad weather conditions orhigh airmass were removed and a nightly average applied, sinceno transit event could be detected and no short-periodic signalwas expected. The data of the two filters show a correlation withsimilar variations (see Fig A.1), but slightly larger errors and alarger apparent amplitude in R band. As visible in Table 3, theaverage uncertainties exceed the rms of the data of both filters.

Besides the photometry provided by the HADES andCARMENES consortia, we analyzed 11 years of observationsfrom Las Campanas Observatory, Chile, with the ASAS-3N sys-tem (All Sky Automated Survey; Pojmanski 1997). The 440 datapoints cover 434 nights from 3 Jun 2006 to 22 Jun 2017. Thisis a timespan of 4037 d with on average one observation every9.2 d. The data also cover season S1, S2, S3, and S5 of ourRV observations, but show average uncertainties similar to theoverall data scatter. The survey delivers a standard magnitude ofVASAS=9.631±0.025 mag using a five-pixel aperture.

We also used MEarth data (Berta et al. 2012) obtained at theFred Lawrence Whipple Observatory on Mount Hopkins, Ari-zona, USA, and provided to us in three sets (see also Díez Alonsoet al. 2019). Set A includes observations on 7 nights from 13 Oct2008 to 3 Mar 2010. Set B includes observations on 53 nightsfrom 19 Nov 2010 to 23 Jun 2011, and is split into the two sea-sons of visibility. Set C covers 79 nights from 22 Oct 2011 to 12



Nov 2015 and overlaps with the S1 to S4 seasons of our spectro-scopic campains (see Fig A.1). We did not find significant peri-odicities of less than 1 d in the data and applied nightly binningafter a 3σ clipping of the values and the uncertainties. The un-certainties are similar to the scatter.

3.3. Spectroscopic indices

We derive a number of additional properties from the HARPS-Nand CARMENES spectra. We do not attempt to mix values fromdifferent instruments and reduction pipelines. An overview anda visualization of all the time-series data after a 3σ-clip on mea-surement uncertainties are given in Table 3 and in the left panelsof Figs. A.2 (HARPS-N) and A.3 (CARMENES). The data aregiven in Table A.1.

We calculate the cross-correlation function (CCF) betweeneach observed spectrum and a binary mask constructed from theaverage spectrum of all observations of Gl 49. The mask is aselection of lines of the combined spectra of certain depth andwidth, excluding regions of strong atmospheric influences. TheCCF is then a representation of the average line of the spectrumin the velocity space and can be approximated by a Gaussianfunction. The variations over time of the velocity span of thehalf-maximum power (FWHM) and the maximum depth or con-trast (CON) of this fit should therefore correlate directly withvelocity variations across the stellar surface. Dumusque et al.(2014) associate such variations with the bright faculae on thestellar surface. We also calculate the bisector inverse slope (BIS,Queloz et al. 2001), which measures the asymmetry of the CCF.This value gives an insight into the structure of the photosphereand should correlate with differences in temperature and pres-sure, both connected strongly to the activity level of the star.The HARPS-N time-series data show in its temporal distribu-tion already that (i) a long-term variation of >1500 d is present inFWHM and CON, (ii) the BIS value is constant, and (iii) S1 doesnot stand out in those indices as in the RVs. The CARMENEStime-series, on the other hand, are less variable over the differentseasons, but match the long-term trend described by the HARPS-N data .

Many magnetically-sensitive line features are present in theoptical spectra of both instruments. They are formed in the hotplasma of the chromosphere and hence vary with the strength ofthe stellar magnetic field. Those features are connected to ele-ments such as calcium, hydrogen, sodium, helium, and iron andare some of the most important tracers for stellar activity in theliterature. The Ca ii ion shows the Fraunhofer H & K emissionlines at 3 934 and 3 969 Å in a domain of low signal-to-noiseratio in the HARPS-N spectra of our target. We quantify thoselines, which measure the lower chromosphere (Gomes da Silvaet al. 2011), with the S index (CaHK; Duncan et al. 1991). Theinfrared triplet of the same ion is located in the CARMENESspectral range at 8 498, 8 542, and 8 662 Å (CaIRT). We measureand sum up their fluxes with respect to the continuum. We addi-tionally measure Hα at 6 563 Å, which is absorbed rather in theupper part of the chromosphere (Gomes da Silva et al. 2011). Weinclude the measurements of the Na iD1 and D2 absorption dou-blet at 5 890 and 5 896 Å. Díaz et al. (2007) proposed that theselines could be used to follow the chromospheric activity levelof very active late-type stars and that they provide good comple-mentary information about the conditions in the middle-to-lowerchromosphere (Mauas 2000). The HARPS-N indices show thelong-period variation >1500 d already described for the RV andCCF index time-series data. In contrast to the CCF indices, the

data points show the different behavior of S1 (marked in greenin Fig. A.2), but with a strong consistency between the differentindices. The CARMENES datasets follow the long-term trendof the HARPS-N sets nicely with an increasing and decreasingactivity level for S5 and S6, respectively.

With the publication of the data reduction pipeline SERVAL,the chromatic index (CRX) was introduced as a new tracer foractivity phenomena (Zechmeister et al. 2018). For every spec-trum, it measures the slope of the RVs calculated for each dif-ferent echelle order when they are plotted against wavelength.A non-zero and time-variable CRX is therefore indicative ofRV variations due to stellar surface phenomena. As shown inFigs. A.2, and A.3 the index follows the long-term trend de-scribed by, for example the FWHM or CON indices.

3.4. Periodogram analysis

In order to get a first overview of the periodicities in our datasets, we use the Generalized Lomb-Scargle (GLS) periodogram(Zechmeister & Kürster 2009). As thresholds for a signal to betentative or significant, we use at this stage the 1 and 0.1 %False Alarm Probability (FAP), respectively, calculated analyti-cally with the formula by Horne & Baliunas (1986). If the signalshows FAPs below those thresholds, we subtract it by a sinu-soidal best-fit and analyze the residuals in the same manner; thisprocedure is customarily dubbed prewhitening. We show the pe-riodograms of the activity tracers in the right panels of Figs. A.1(photometry), A.2 (HARPS-N), and A.3 (CARMENES). Thered horizontal lines indicate the 0.1, 1, and 10 % FAP level,whereas the two orange vertical lines stand for the periodicitiesat 9.37 and 18.86 d, and the red vertical line marks the period at13.85 d. All significant signals of the different datasets are givenin Table 3 under Prot, if the signal is close to the 19-d period, andunder Padd, if it is not. We mark the first with *, if the signal isdistinguishable from the noise but above the 1 % FAP level.

The various photometric datasets reveal the period at 18.9 dvery well. The first harmonic, on the other hand, is mostly notseen due to the low amplitude of the signal. For the EXORAP fil-ters, the data show an additional strong periodicity at >220 d. Weconnect it in part to the time-sampling, but also identify the vari-ation in the data point distribution. The SNO photometry showsalso a strong periodicity of >150 d. In the noisy ASAS-3 data,we are able to see two statistically insignificant periodicities at18.94 d and 177 d. Also from MEarth data we are able to clearlydetect the rotational periodicity.

It seems that the HARPS-N S1 data are dominated by astrongly varying activity level of Gl 49. This is seen clearly inthe large RV scatter of that season (Fig. 1) and in the varying val-ues for the chromospheric indices (Fig. A.2). Since in all casesthis data subset (20% of all data points) only adds noise to theperiodograms, we exclude it from this analysis and the peri-odograms in Fig. A.2. Even then, we identify in only four indiceslow-significance signals at periodicities of 19 or 9.4 d. All setsshow at 400 to 425 d a signal which is probably a combinationof the long-term periodicity of >1500 d and the strong yearlyalias created by the seasons of visibility as seen in the windowfunction, that is, the Fourier transformation of the time-sampling(see Fig. 1). Given the distribution of all the HARPS-N datasets,we assume the star to be magnetically rather active at S2 and bythe end of S5. The minimum activity level at approximately Mar2015 (BJD=2 457 100 d) coincides with the minimum scatter inthe RV measurements. But such signals of longer periods are notfound by the periodogram approach.



Table 3. Basic properties and results of the analysis of different photometric observations (top), and activity indices of HARPS-N (middle) andCARMENES observations (bottom).

Basic properties Periodogram analysis GP with celerite GP with georgeIndex rms error unit Prot [d] Padd [d] Phase [deg] PSHO [d] Plife [d] ∆ ln L PQP [d] λ [d] w ∆ ln L

EXORAP ∆B 17.1 1.3 mmag 18.87 221.6 50.2 17.07 43.1 62.9 18.86 73.7 1.1 102.7EXORAP ∆V 14.6 1.5 mmag 18.91 259.1 41.4 18.24 39.0 21.3 19.16 133.7 3.2 307.0EXORAP ∆R 13.4 1.4 mmag 18.85 240.3 78.0 18.54 53.2 37.3 18.81 66.1 1.3 56.0EXORAP ∆I 14.3 1.0 mmag 18.94 623.3 61.7 20.50 31.6 47.6 18.98 48.8 1.2 90.1SNO ∆V 10.3 13.0 mmag 18.86 173.8 343.2 19.03 302.4 215.1 19.25 429.5 2.3 213.3SNO ∆R 10.3 16.8 mmag 19.04 133.3 336.1 19.21 278.6 199.1 19.09 239.5 2.3 197.3ASAS ∆V 25.3 27.4 mmag * ... 77.0 ... ... ... ... ... ... ...MEarth ∆V 7.8 7.3 mmag 20.02 ... 51.7 ... ... ... ... ... ... ...

CaHK 0.350 0.014 ... ... 419.3 79.3 19.98 60.5 20.0 19.75 157.2 2.6 39.0Hα 0.0284 0.0021 ... ... 409.0 77.2 21.93 35.6 46.2 18.72 263.8 0.7 57.4NaI 0.0052 0.0034 ... * 416.6 80.7 ... ... ... ... ... ... ...FWHM 0.040 0.013 km s−1 ... >3060 117.5 ... ... ... ... ... ... ...CON 0.453 0.081 % * 425.5 229.6 ... ... ... ... ... ... ...BIS 2.04 0.69 m s−1 19.1 ... 356.0 ... ... ... 18.62 110.5 6.1 13.5CRX 10.5 7.9 m s−1 N−1

p * >3062 227.1 ... ... ... 18.91 762.3 1.3 12.2

CaIRT 0.0417 0.0025 ... 18.8 538.6 79.8 18.99 83.2 16.8 18.98 166.0 2.3 25.8Hα 0.0300 0.0015 ... 18.8 569.1 76.7 18.63 234.9 15.4 18.90 225.2 2.7 27.2NaI 0.0390 0.0040 ... * 437.1 66.0 ... ... ... 18.17 111.1 3.4 12.7FWHM 0.019 0.021 km s−1 7.6 >920 50.6 ... ... ... ... ... ... ...CON 0.124 0.060 % * 428.9 18.5 ... ... ... 19.10 140.8 1.0 13.6BIS 6.33 0.94 m s−1 18.3 ... 121.7 18.59 60.5 11.1 18.91 55.5 1.8 14.3CRX 20.8 14.5 m s−1 N−1

p * 90.3 243.9 ... ... ... ... ... ... ...

Notes. We show the tentative and significant signals of the periodogram analysis, and the parameters of the best solution of an MCMC approachusing Gaussian Processes (GP) with the simple harmonic oscillator (SHO) as kernel and the celerite code, and with the quasi-periodic harmonicoscillator (QP) as kernel of the george package (see Sect. 3.7 for the definition of the hyper-parameters). The ∆ ln L value refers to the differencein ln L compared to a null model.*: the peak in the periodogram connected to the rotational period of Gl 49 stands out from the noise, but does not reach the 1% FAP level.

The data distribution of the CARMENES indices is, on av-erage, very similar between the S4 to S6 seasons, but indicat-ing a slightly less active state of Gl 49 by the end of S6 thanin the previous seasons and showing an upward/downward trendfor S5/S6. More clearly than for the HARPS-N indices, all theirperiodograms show features at around 19, and/or 9.4 d, whichmight in part be due to the higher observing cadence. The aliasat 365.25 d shows at longer periods since it is probably as wellmixed with the unresolved long-term trend already described.

3.5. Secondary periodicity

The presence and stellar nature of a 19-d periodicity and its firstharmonic in Gl 49 is shown by the periodogram analysis of thedifferent activity indices. It is also reflected in the strong symme-try that the periodograms of especially the CARMENES CaIRTand Hα indices show around this period (see Fig. A.3 and, for thelatter, bottom panel of Fig. 2). Besides the important periodici-ties at P=18.86 d (orange) and 13.85 d (red), we show the yearlyaliases of the first at frequencies P−1±365.25 d−1 (black dashedlines) and the analytical 0.1, 1, and 10 % FAP levels (from topto bottom) with the blue horizontal dashed lines. The symme-try results most prominently in two periodogram peaks abovethe noise level at 14.1 and 28.3 d (green lines), correspond-ing to frequencies P−1±56.44 d−1. This is indicative of a 56 d-variation of the amplitude of the 19 d-signal. We show the wholeCARMENES dataset in the top panel of Fig. 2 in blue. Sincethis secondary periodicity is not seen in the periodogram but thevalue is close to 3P, we fold the data to 56.44 days and show itin the middle panel. There, we observe one very strong varia-tion between phase 0.3 and 0.8, proving this secondary 56.4 d-

periodicity of the stellar contribution at S5 and S6. We also in-clude the HARPS-N values, after the fit of an adequate offset,which then fall nicely in our picture.

3.6. Time-series data correlations

We identify the rotational periodicity in all photometric and mostof the activity index datasets, as well as in the RVs (see Sect. 4).To be able to study their possible correlations, we calculate thephase shifts with respect to the RV time-series data (Suárez Mas-careño et al. 2017b). To achieve this, we apply to all sets thebest-fit period of the interval of 18 d< P <20 d of the RV dataof HARPS-N (19.11 d), and CARMENES (18.92 d), respectively(see Table 2). The resulting shift for each set is given in Table 3.We expect only the FWHM and CON values to be in phase withthe RVs. For all other indices, including the photometry, a phaseshift of 45 deg is theoretically expected for a simple one-starspotmodel. A closer look at the results reveals a great consistencyonly for the chromospheric indices with phase shifts between 75and 80 deg and for the CRX index with 230 to 240 deg. We addi-tionally calculate the phase shifts of the photometric data usingthe HARPS-N periodicity. The results are pointing to a value of40 to 80 deg. The differences for the SNO photometry could berelated to an unknown activity event after S6 season.

We then calculate Spearman and Pearson factors and findhighly significant linear correlations only for the chromosphericindices. For CaHK/CaIRT and Hα indices we obtain Spearmanfactors of 0.88 and 0.95 for HARPS-N and CARMENES data,respectively. For the calcium index with the Na i index it is 0.73and 0.68, and for Hα with the sodium index, we calculate 0.70and 0.72. We do not find correlations for any other combination



1400 1600 1800 2000 2200BJD [d] - 2456000

0.1

0.0

0.1

rela

tive

H in

dex

0.0 0.2 0.4 0.6 0.8 1.0Phase

0.1

0.0

0.1

rela

tive

H in

dex

0.03 0.04 0.05 0.06 0.07Frequency [d 1]

0.0

0.2

0.4

Powe

r

Fig. 2. Hα time-series data of CARMENES (blue) and HARPS-N (red)spectra of seasons S4, S5, and S6 (top panel). In the middle panel, weshow the data points phase folded to 56.44 d. The bottom panel showsthe periodogram of the CARMENES index as in Fig. A.3. We mark theperiods at 18.86 (orange vertical line) and 13.85 d (red vertical line),their yearly aliases at frequencies ν = P−1± 365.25−1 d−1 (black verticaldashed lines), and the frequencies at 18.86−1±56.44−1 d−1 (green verti-cal lines), where the symmetry of the index data strongly imprints. The0.1, 1, and 10 % FAP levels are shown by the blue horizontal dashedlines (from top to bottom).

of time-series data which is, in most cases, due to the large phaseshifts between them. Linear correlations are also not found forRV/CON of CARMENES or CRX/CON of HARPS-N observa-tions, where small phase differences are measured.

To illustrate this procedure, we show the correlations of theRV data with the chromospheric activity indices in Fig. 3. Weshow, from left to right, the calcium, Hα, and NaI indices forHARPS-N (top panels), and CARMENES (bottom panels) in-struments, respectively.

3.7. Modeling correlated noise

The phase shifts of the activity indices imply that the informationabout the stellar contribution monitored by each index cannot beimplemented easily in the modeling of our RV data. We thereforemodel the data as correlated noise using GP regression (Rajpaulet al. 2015; Roberts et al. 2013). The covariance matrix of theGP contains all the information we want to apply regarding thecorrelation of the data points, which is consistent with the pic-ture of active regions rotating with the star and evolving in time.In our case this is a quasi-periodic kernel, similar to a dampedoscillator, consisting of a periodic component (described by hy-perparameters period PQP and amplitude KQP) connected to therotational period of the star, and a damping factor λ (connectedto the evolutionary timescale of surface phenomena). An addi-tional scaling factor w (generally <1) is used to measure the re-

2.0 2.5 3.0CaHK

10

0

10

20

RV [m

s1 ]

SP: 0.152 (0.076)PE: 0.190 (0.026)

0.9 1.0 1.1H

SP: 0.141 (0.100)PE: 0.122 (0.157)

1.15 1.20NaI

SP: 0.160 (0.062)PE: 0.177 (0.039)

1.6 1.7 1.8CaIRT

10

0

10

20

RV [m

s1 ]

SP: 0.073 (0.538)PE: 0.042 (0.721)

0.85 0.90 0.95H

SP: 0.105 (0.378)PE: 0.096 (0.419)

0.5 0.6NaI

SP: 0.154 (0.197)PE: 0.152 (0.202)

Fig. 3. Correlations of the HARPS-N (top) and CARMENES (bottom)chromospheric line indices with the RV data. The red ellipses show thetheoretical phase-shifted correlation using the phase shifts of periodic-ities of 19.11 d (HARPS-N) and 18.92 d (CARMENES), respectively,and the rms of the data as the amplitude of the signal. The blue linesrepresent the best linear fit, i.e., the Spearman factor. At the top of eachimage, we show the Spearman (SP) and Pearson (PE) factors togetherwith their respective p-values in parenthesis.

lation of both kernel parts. A small w shows large influence ofPQP and small influence of the exponential decay. Details on thismethod using the george code by D. Foreman-Mackey are de-scribed in Ambikasaran et al. (2015). In another approach, weuse the celerite code by the same authors calculating the co-variance matrix by a stochastically-driven simple harmonic os-cillator (Foreman-Mackey et al. 2017). In this code, we modelthe rotational periodicity with the two hyperparameters PSHO andKSHO, and a hyperparameter Plife, which is supposed to be con-nected to the lifetime of the active regions.

To compare different models consistently, we use the log-likelihood (ln L) statistics (e.g., Anglada-Escudé et al. 2013).The likelihood function shows the probability of the data match-ing a certain model, and generalizes the χ2 statistics introducingan additional jitter term σ. Details on the definition for a givendataset and noise models are given in, for example Ribas et al.(2018). At this point, we consider a model compared to a sec-ond one (including a null model) significant if the differencein ln L exceeds a value of 15, which roughly corresponds to aFAP of 0.1% in our measurements. A difference of ∆ ln L <5 isconsidered noise. For a detailed discussion on the usage of thisstatistics for model comparison see Baluev (2013), and for an ex-ample implementation of this procedure see Ribas et al. (2018).We provide later (see Sect. 4.2) an empirical determination ofthe ∆ ln L threshold required to claim a confident detection for acertain model.

We further use the emcee code by D. Foreman-Mackey toexplore the parameter space with a Markov Chain Monte Carloprocedure (MCMC, Foreman-Mackey et al. 2013). For the indextime-series data, we apply 10 000 steps on each of 100 walkers.As a comparison, we maximize the ln L using only offset andadditional jitter (null-model). In a second step, we then applythe two GP algorithms and their respective covariance matrices.Since we already know the rotational period of Gl 49 and haveanalyzed both the periodograms and the temporal distribution of



the different time-series data, we set boundaries to the uniformpriors of the hyperparameters PQP and PSHO from 5 to 30 d. Toall other priors we apply reasonable boundaries (see Tab. 5). Weshow the results of this procedure in Table 3 only if ∆ ln L >10compared to the null model, to include also tentative solutions.

The CaHK/CaIRT and Hα indices and the EXORAP pho-tometry show the most significant and consistent results and re-trieve the rotational period very well. But the interpretation ofthe hyperparameters Plife, λ, and w is more complicated. As al-ready mentioned, w �1 (e.g., HARPS-N BIS) shows the dom-inance of the exponential decay over the rotation. On the otherhand, λ and Plife are generally not constrained consistently. Thelatter shows best solutions in the interval 2Prot–4Prot for nearlyall the indices, confirming the value of 56.4 d, which we foundas secondary period in some activity indices of seasons S5 andS6. λ could instead be summarized as an overall larger valueworking similar for the EXORAP photometry (2Prot–4Prot), butshowing instead 150 to 250 d for the other activity indices. Thelarge values for the SNO photometry are due to the stability ofthe 19 d-variation over the short time-period of the observations.

4. Radial velocities

4.1. First analysis of individual datasets

To get an overview of the periodicities present in the differ-ent RV datasets, we analyze them individually with the GLSmethod and show the results in Fig. 1 for HIRES, HARPS-N,and CARMENES. The 21 HIRES S0 RV measurements are notable to sample the periodicities that we are interested in, sincea lower limit is given by the Nyquist frequency with approxi-mately 160 d. If we search for periodicities down to 1 d, we find,however, a tentative signal at 17.3 d (see column 7 of Table 2),which we assume to be connected to the rotational period. Inthe prewhitening process using the 137 epochs of HARPS-N S1to S6 observations, we find three strong signals in the range ofthe rotational period at 18.3, 18.9, and 19.1 d. Furthermore, wefind a periodicity close to the first harmonic of the rotation at ap-proximately 9.6 d. The facts that those periodicities are not eas-ily cleaned with simple sinusoidal fits, that they influence eachother, and that the supposed harmonic is so prominent point to arather complex RV contribution from the stellar rotation. The ad-ditional significant signal at 13.4 d will be shown to be connectedto the orbital period of a proposed planet. If we exclude the S1dataset in our prewhitening periodogram analysis, the main peri-odicity of the remaining dataset is now the 9.4 d-signal, as shownin Table 2, and a signal at 14.4 d rather than 13.4 d. The S1 seasonwith the large data scatter shows significant peaks in all three in-teresting time intervals of the periodogram. The analysis of the80 CARMENES data points reveals three periodicities at 9.3,18.9, and 14.4 d. The periodogram is more easily cleaned by si-nusoidal fits, indicating a more stable stellar contribution in S4to S6 than in S1 to S3. Like in the case of the S2-S6 HARPS-N set, the most prominent periodicity in the CARMENES set isconnected to the first harmonic of the rotational period, that is9.3 d, and a remaining signal at 14.4 d is visible.

4.2. Methodology

Using the knowledge of the periodicities in our datasets, wesearch in the following for the best model to fit to the RV data,in order to estimate the best parameters for a possible planetarycompanion. We exclude the first season of the HARPS-N obser-vations, since we assume it to be affected by strong variations

Table 4. Overview of the statistics on the different models applied tothe RV data of Gl 49 from HARPS-N S2-S6 and CARMENES datasets.

Nsignal model Nparam ln L0 null 4 −574.0

null + GPQP 8 −519.9null + GPSHO 7 −527.8

1 Keplerian 9 −550.9Keplerian + GPQP 13 −491.8

seasonal 20 −491.3circular 11 −496.9with HARPS-N S1 15 (−499.3)with HIRES/HARPS-N S1 17 (−502.3)

Keplerian + GPSHO 12 −492.2

2 Keplerians 14 −531.7Keplerians + GPQP 18 −482.8Keplerians + GPSHO 17 −484.7

3 Keplerians 19 −514.4



Notes. Nsignal indicates the number of fitted Keplerians; Nparam shows thenumber of parameters fitted in the respective model; GPQP and GPSHOare the GP noise terms with the quasi-periodic kernel of george and theharmonic oscillator kernel of celerite, respectively. The parenthesisindicate the ln L values interpolated from fits with different number ofdata points.

in stellar activity. We exclude further the HIRES data, since thetime sampling was very poor. Although the rotational period inthe RVs is very stable, these datasets add more noise than signalto our data and might distort any fitted parameter. In the follow-ing search for the most probable fit parameters, we then use theHARPS-N S2 to S6 and CARMENES datasets, that is 80 % ofthe available RV measurements.

We fit different models to be able to compare their probabil-ities through their ln L values. We use again the emcee code forthe MCMC analysis as outlined in Sect. 3.7 with 100 walkers and10 000 steps. If not mentioned differently, uniform priors withreasonable boundaries (see Table 5) are used, in order to fullyexploit the parameter space and to have a different approach toevaluate the probability and significance of the fitted parameters.

We begin by fitting a constant null model, including off-sets and jitters for both datasets, and obtaining as a best-fitln L=−574.0 (see Table 4). Based on this value, we built a pe-riodogram by considering for every period the Keplerian fit(f(P,K,e sinω, e cosω, Tper), with P as period, K as amplitude, eas eccentricity, ω as true anomaly, and Tper as time of periastronof the respective orbit) with the largest ln L. As a significancetest, we apply bootstrap randomization (Murdoch et al. 1993)using 10 000 permutations of the data points and reach a 0.1 and1% FAP with ∆ ln L=16.11 and 14.24, respectively. Those num-bers confirm the ∆ ln L=15 mentioned above, but are connectedto the one-Keplerian model. Nevertheless, in the following, amore detailed model, that is including a correlated noise term(GP) or an additional Keplerian (Kep), is considered tentativeor significant if it shows ∆ ln L >14.24 and 16.11, respectively,compared to the former model. We also confirm the noise levelof ∆ ln L=5.



0

20lnL

75 25 15 10 8Period [day]

0

25lnL

0

20

lnL

0

20

lnL

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14Frequency [day 1]

0

10lnL

Fig. 4. Region of interest of the ∆ ln L periodograms of the combinedRV datasets of Gl 49 (HARPS-N S2-S6 and CARMENES). The toppanel shows the difference in ln L from a linear null model includingadditional jitters and offsets, to a model including one additional Keple-rian (top panel). The following panel then shows the periodogram of thedata with this Keplerian subtracted. From the second panel from the topto the bottom panel we have subtracted a best-fit Keplerian curve withperiods of 9.37, 19.09, 13.85 and 18.91 d, as shown by the broad redvertical lines. The dashed horizontal red lines indicate the 0.1 and1%FAP values calculated by bootstrap randomization using a single Kep-lerian model on the datasets (see Sect. 4.2).

4.3. Beat frequency

Disregarding the knowledge of the nature of the RV signals, wefit up to five Keplerians to the datasets. Each of those curvesimplements five fit parameters, which add to the four parametersalready in place for RV offsets and additional jitters (see Nparamin Table 4). The increase in ln L is significant until the fourthKeplerian (ln L4Kep − ln L3Kep >16.11), where 24 parameters arefitted. The periodograms of this procedure are shown in Fig. 4 forthe whole dataset, and the residuals after subtracting the best-fitof one, two and three Keplerians, respectively.

As a result, the four-Keplerian model (see top panel of Fig. 5)is able to clean the periodogram significantly, and it describes thepossible stellar contribution with three Keplerian curves (middlepanel) with periods of 9.37, 19.09, and 18.91 d. For the sakesof this exercise, the best fit and the largest ln L, this proce-dure should be as valid as the usual fit of simple- or double-sinusoidal models for the complex and noisy stellar contribution.The remaining periodicity at 13.85 d is hidden behind those threestrong signals but identified significantly as the third Keplerian.In the best MCMC solution, this periodicity shows a RV am-plitude of 2.71 m s−1, and an eccentricity of 0.36. Actually, theresults are very similar to the outcome of the prewhitening pro-cess of the individual datasets using sinusoids in Sect. 4.1. We

10

0

10

RV [m

s1 ]

a) 4 Kep

10

0

10

RV [m

s1 ]

b) 3 Kep

600 800 1000 1200 1400 1600 1800 2000 2200BJD [d] -2456000

10

0

10

RV [m

s1 ]

c) 19.09d + 18.91d (2042d)

Fig. 5. Four Keplerian best-fit models including all signals (periods of9.4, 13.9, 18.9, and 19.1 d; top panel), the three signals connected tothe stellar rotation (9.4, 18.9, and 19.1 d; middle panel), and the twosignals around 19 d (bottom panel), resulting in the amplitude variationfollowing the beat frequency of 2042 d in the observed time span. Thered dots are the HARPS-N S2 to S6 and CARMENES data points.

see strong yearly aliases on all peaks in the periodograms show-ing the response of the time-sampling as seen in the windowfunction.

Whereas we can explain the signal at 9.4 d as the first har-monic of the rotational period, we show the combination ofthe two periods around 19 d in the bottom panel of Fig. 5. Thetwo periodicities are effectively responsible for an amplitudechange of the 19 d-signal, which occurs with the beat frequencyat ((19.09)−1+(18.91)−1)−1=2042 d. We identify this periodicityto be the same as the long-term variation of >1500 d already de-scribed, especially in the HARPS-N datasets (see Figs. 1, A.2).

4.4. Models including correlated RV noise

A more physical approach to model the stellar contribution isthe application of a GP noise term. Here, as already explained,we model the stellar contribution as correlated noise througha covariance matrix representing a damped or stochasticallydriven harmonic oscillator. In Table 4 we can see that the mod-els are significant including also one Keplerian (ln L1Kep+GP −

ln LGP >16.11) and fitting thereby 13 and 12 parameters forgeorge and celerite codes, respectively. Including a sec-ond Keplerian is statistically not important with ln L2Kep+GP −

ln L1Kep+GP <14.24. With the wide prior boundaries as shownin Table 5, the best model captures both the rotational and the13.85 d-signal as expected, indicating the difference in stabil-ity of those two periodicities. The more flexible quasi-periodickernel delivers a larger best ln L value in the MCMC analysisthan the simple harmonic oscillator kernel, and we reach up to−491.8, resulting in a high significance of∆ ln L=59.1 for ourproposed system and planet. If we compare the results with themodel of the four Keplerians of Sect. 4.3, the amplitude of the13.85 d-signal decreases slightly to 2.54 m s−1. Such a suppres-sion of the RV amplitude is suspected to be a disadvantage of theGP regression (Feng et al. 2016; Ribas et al. 2018).

We carry out some additional tests with the selected bestmodel of one Keplerian plus a GP noise term using the quasi-periodic kernel (see Table 4):

– We assume seasonal RV shifts for each instrument and in-clude in our models RV offsets for every season and instru-



0.0 0.2 0.4 0.6 0.8 1.0Phase

5

0

5

10

RV [m

s1 ]

Fig. 6. RV data from HARPS-N (green) and CARMENES (red) of Gl 49of seasons S2 to S6 without the stellar contribution as calculated by theGP term of the final model. The data are folded in phase to 13.85 d toillustrate the impact of the Keplerian orbit as shown by the black line.

ment. The period ranges that we are interested in should notbe affected. As we increase the number of parameters from13 to 20, we achieve practically no change in ln L. This iscertainly partially due to the absorption of the effect by theGP.

– If we apply our model with a circular rather than an eccen-tric Keplerian orbit, we reach ln L=−496.9. Comparing thiscircular model to the eccentric model, we reach a differenceof ∆ ln L=5.1, which is close to the noise level of ∆ ln L=5.

– If we include HARPS-N S1 data, the RV amplitude dropsand the eccentricity slightly rises. We reach ln L=−570.3 for217 data points, which translates to ln L=−499.3 for 190 datapoints, indicating a worsening of the fit.

– If we include both HARPS-N S1 and HIRES data, the trendsfor RV amplitude and eccentricity continue. We reach atranslated ln L value of−502.3.

The results of those exercises reflect the stability of bothHARPS-N and CARMENES instruments, the unusual behaviorof HARPS-N S1 data, and the poor sampling of the HIRES data.

4.5. Final model

To find the most probable values and uncertainties for the 13 freeparameters of our selected best model including one Keplerianand a GP noise term, we explore 250×50 000 solutions of ourMCMC analysis. We use ln L=−519.9 of the GPQP-term best-fit(see Table 4) as a reference value. We have 10 076 912 solutionsabove that limit. Including a Keplerian, we consider every so-lution as tentative which adds to this number at least the 1%FAP of ∆ ln L, which is 14.24. The application of such a limit ofln L > −505.7 leaves us with 8 156 650 solutions, which we usefor our statistical approach. Our most probable model parame-ters and their uncertainties are then the median and the rms ofthe selected solutions, respectively. We show the distribution ofall tentative solutions for the fitted parameters in the corner plotin Fig. A.4 and give the final values in Table 5. Additionally, weshow the ln L distribution per parameter of interest in Fig. 7. Thetime series of the final model is shown in Fig. 8 and the phase-folded curve excluding the stellar GP contribution in Fig. 6.

The periods of both the GP term and the Keplerian signal areconstrained very precisely, although we can see still some struc-ture well below any noise level in the ln L distribution of the for-mer (see Fig. 7). This is also visible in the corner plot (Fig. A.4),where this parameter shows a slight correlation with λ, which

Table 5. Fitted and derived planetary and stellar parameters.

Parameter Priors Results

Fitted Keplerian parameters

P [d] 0-2·∆T 13.8508+0.0053−0.0051

K [m s−1] 0-10·rms 2.52+0.31−0.30

e sinω ±1 0.23+0.11−0.12

e cosω ±1 0.25+0.11−0.12

Tperi [d] ±∆T 2455995.88+0.72−0.78

Derived planetary parameters

e 0.363+0.099−0.096

ω [deg] 43±21M sin i [M⊕] 5.63+0.67

−0.68

a [au] 0.0905±0.0011

Fitted hyper-parameters of quasi-periodic kernel

PQP [d] 0-2·∆T 18.864+0.103−0.085

KQP [m s−1] 0-10·rms 3.02+0.25−0.23

w 0-10 4.3+1.4−1.1

λ [d] 0-2·∆T 150+90−50

Fitted RV offsets and additional jitters

γHN26 [m s−1] ±10·rms 0.1+1.1−1.2

γCA [m s−1] ±10·rms 0.6±1.5σHN26 [m s−1] 0-10·rms 0.76+0.31

−0.32

σCA [m s−1] 0-10·rms 1.41+0.39−0.45

Notes. We show the fitted and derived parameters for the proposed plan-etary companion, the GP hyper-parameters of the quasi-periodic kernelfor the correlated stellar RV contribution, and the offsets and jitters ofthe two RV datasets (HARPS-N S2 to S6, CARMENES) used for thebest model of Gl 49. The uniform priors are all limited by the largestreasonable boundaries.

also shows this insignificant bi-modal behavior. The eccentricityof the Keplerian orbit is fitted by e sinω and e cosω and adds upto 0.36. We see evidence at this point for the parameters to be dif-ferent from 0 within ∆ ln L <5 from the peak value. The variableGP-term contribution, which corresponds to the more significantrotational signal, shows a larger impact on the RV data with anamplitude of 3.02 m s−1 than the stable contribution of the Keple-rian with 2.52 m s−1. The important values of λ and w are ratherpoorly constrained. We calculate for the first a value between100 and 240 d. The median value of w is 4.3. Although beyondthe normal limits used for this parameter, it illustrates the impor-tance of the exponential decay, that is the variation in amplitude,of the signal in comparison to its strong periodic behavior. Theadditional jitters of our best-fit model show a consistent pictureand correlate with the uncertainties of the measurements alreadyin place.

5. Discussion

5.1. The planet Gl 49b

We find evidence of a 13.85 d-signal in our RV data. We haveshown that the signal and its yearly aliases are not present inany activity index, and that it is not introduced by the time-sampling or the data treatment using a variety of datasets and



Fig. 7. ∆ ln L distribution of the different parameters of interest following the MCMC procedure. The zero level is set at the best-fit ln L of themodel including only the GPQP term (see Table 4). We show the 0.1 and 1 % FAP level calculated by bootstrap randomization using a singleKeplerian model (Sect. 4.2) by the black horizontal lines. The black vertical lines in each image indicate the median value and the rms of eachparameter considering the solutions below the 1 % FAP level. Whereas the green dots indicate all solutions, we plot in red every 1 000, and inyellow every 10 000 solutions, in order to get an insight on the density distribution. We note, that for M sin i and a, the uncertainty of the mass ofGl 49 is considered.

models. In comparison to the 18.86 d-signal also present in thedata, it shows great stability in period, phase, and amplitude overthe six years of observations used for the final fit. We thereforeassume this periodicity to be caused by the orbital motion of aplanetary companion Gl 49b. Its impact on the RV data of its hoststar is deeply hidden inside the complex stellar activity-inducedvariations. But with our best noise model, by which we accu-rately correct for the stellar contribution, the planetary signalis strong, with a significance of ∆ ln L=59.1, and the parame-

ters are very well constrained. We find the companion to have aminimum mass of 5.6±0.7 M⊕. The super-earth orbits its star in13.851±0.005 d in an eccentric orbit (e=0.36±0.10) with a semi-major axis of 0.090±0.001 au. The amplitude it introduces in ourRV time-series data is 2.5±0.3 m s−1.

Although not significantly distinguishable from the noise inour GP approach (see Table 4), we consider the eccentricity ofthe orbit of Gl 49b in our final fit. This is because the distribu-tions of the fitted parameters e sinω and e cosω (see Fig. A.4),



10

5

0

5

10

15

RV [m

s1 ]

600 800 1000 1200 1400 1600 1800 2000 2200BJD [d]-2456000

505

O-C

[ms

1 ]

Fig. 8. RV data of Gl 49 of season S2 to S6 of HARPS-N (green) and CARMENES (red), and the final model (black line) and its 1σ uncertainties(gray lines) including a GP term and a quasi-periodic kernel for the stellar contribution and a Keplerian curve. In the bottom panel, the residualsare shown.

and of the derived eccentricity (see Fig. 7) are different from zeroat the ∆ ln L=5 level. Another argument is that in the prewhiten-ing process of the individual datasets (see Table 2) we detect theyearly aliases at 13.34, and 14.40 d rather than the orbital periodof Gl 49b at 13.85 d. Additionally, the eccentricity of the Keple-rian signal is very persistent in the variety of models fitted to thewhole dataset and to all subsets.

5.2. Stellar rotation

We confirm the rotational period of Gl 49 to be 18.86+0.10−0.09 d and

refine thereby the previously reported values by Suárez Mas-careño et al. (2018) and Díez Alonso et al. (2019). The signalinduced into the RV data is very stable in phase and period inall the different data subsets, but not in amplitude. The idea isthat those variations are caused by surface phenomena, whichco-rotate independently of their latitude with the rotational pe-riod but appear always at specific longitudes in order to explainthe stable phase of the signal (see e.g., Strassmeier 2009). Thestrong presence of the first harmonic at 9.4 d and its persistencein the prewhitening process shows the non-Keplerian nature ofthis contribution. The effect can be best explained if we assumethe stellar surface to be dark-spot dominated and that the brighthot faculae with their effective convective blueshift have only lit-tle influence on the measured RVs. This is expected for low-massstars and should be also in part responsible for the CCF indicesnot showing great consistency in this study. In the S1 season, onthe other hand, where the activity contribution varies, and wherethe rotational influence dominates, the impact of faculae is sup-posed to be stronger.

The periodicity is also detected strongly in the photomet-ric data, where the difference in impact on the different filtersshows its non-planetary nature. In general, the photometric dataare shifted in phase with respect to the RV data by 40 to 80 deg.We detect the rotational signal also in the prewhitening of theCARMENES activity indices, and in four out of seven HARPS-N indices. A closer look with our GP noise model then re-veals the presence of the periodicity in all the chromospheric in-dices. Those datasets show consistent phase shifts of 75–80 deg,which is in an expected range following Suárez Mascareño et al.(2017b), and slightly smaller than the 120 deg that we measuredfor the less active GJ 3942 (Perger et al. 2017b). The CRX indexshows a phase shift of 230 to 240 deg.

5.3. Evolutionary timescale of dark spots

Signals of a few rotational periods to some hundred days are de-tected in many of the datasets, which should be at least in partdue to the evolutionary time scales of activity phenomena onthe surface of Gl 49. Most consistent values of the prewhiten-ing process are delivered by the chromospheric activity indiceswith periodicities of 400 to 550 d. Such signals are not foundin the RVs. To find better constraints on those timescales wefitted our GP model including Plife and λ, but we still lack adefinitive translation of those hyper-parameters to the physicalworld. For the first, we find values from 40 to 80 d in photome-try and chromospheric indices, which match the value of 56.4 dthat we found for the amplitude variation of the 19 d-signal in S5and S6. Whereas the EXORAP photometry finds similar valuesfor λ, the line features set the parameter in a range from 150 to250 d. Since the covariance matrix represents a periodic modelwith changing apparent amplitude, which is exactly what we seein the RV data, we can most certainly trust the λ=150 d foundby the analysis of those datasets, but scale it down by the Eulernumber e=2.71828..., in order to match Plife. Those values thenare also in agreement with the decay times found for M dwarfsin Giles et al. (2017). The stellar origin of those periodicities canalso be seen by the impact shown on the SNO and EXORAPdata on this time scale. The contrast of those spots against thephotosphere seems to decrease with increasing wavelengths.

5.4. Evolution of stellar activity

The impact of stellar activity on our datasets goes even further,since the RV data shows a varying scatter over the different sea-sons, which we find to be connected to the amplitude of the stel-lar signal changing on time scales of at least 1500 d. We can tracethis pattern in all activity indices showing that actual evolutionof stellar activity is in progress, and the long-term variation isnot found in the frequentist analysis of our data. Instead, thissignal can be mathematically described by two Keplerian curvesof slightly different periods, resulting in a signal with an ampli-tude that changes on a 2000 d-time scale as a consequence of thebeat frequency. As already mentioned, this is exactly what thecovariance matrices of the GP noise terms represent and how weexplain the high value of w in our GP approach with the quasi-periodic kernel. As a consequence of this study, we assume Gl 49



in S1 to be at a moderate, yet strongly varying activity level, per-haps influenced by bright faculae. The activity level increaseduntil the peak level in S2, which was manifested by an increasein spot number or in the motion of the spots to higher latitudes.Then, until March 2015 (BJD=2 457 100 d, S3-S4), the activitylevel dropped. A small increase until S5-S6 was followed by adecreasing level in 2018. This also explains that the activity andplanetary signals in all CARMENES data are more stable thanin the observations before S4.

6. Conclusions and final remarks

In this study, we analyzed 21.5 years of time-series data of theM1.5 V-type star Gl 49. Those include 137 epochs of spectro-scopic observations with the HARPS-N instrument within theHADES framework, 80 epochs within the M-dwarf program ofthe CARMENES spectrograph, and additional 21 epochs fromarchived spectra observed with the HIRES instrument. Only thecombination of HARPS-N and CARMENES data and their in-depth analysis made it possible to disentangle the contributionof the exoplanet Gl 49b from the complex activity contributionsof its host star. We furthermore studied simultaneous photomet-ric multiband observations made within the EXORAP program,with the T90 telescope at SNO, and using archival MEarth andASAS photometry.

We discover the super-Earth Gl 49b, which orbits its host starwith a period of 13.9 d in an eccentric orbit (e=0.36) and with asemi-major axis of 0.090 au. In all the different datasets that wehave analyzed in this study, this periodicity is only present inthe RV time series. The rotational velocity that we are able tocalculate from the radius and rotational period of the star is inagreement with the upper limit of 2 km s−1 given for v sin i in Ta-ble 1, pointing to an orbital inclination close to 90 deg for an orbitclose to the equatorial plane of Gl 49. We therefore calculate thegeometric transit probability with 2.0 % as a lower limit. Such anevent would take approximately 2.5 h and show a depth of 1 to2.5 mmag for a rocky/gaseous nature of Gl 49b with Rp=1.8 and3 R⊕. This is challenging to detect with ground-based telescopes,but could be a good target for TESS (Transiting Exoplanet Sur-vey Satellite; Ricker et al. 2015), or the upcoming CHEOPSmission (CHaracterising ExOPlanet Satellite; Broeg et al. 2014).We furthermore calculate an equilibrium temperature of approx-imately 350 K for the planet assuming zero albedo and using theradiative equilibrium temperature Tp,eq.=Tstar,eff(0.5 · Rstar/a)0.5.Gl 49b orbits well interior to the ’recent Venus’ habitable zone atapproximately 0.18 au (see Table 1). In the search for exoplanetsaround M dwarfs, Gl 49b has characteristics similar to other re-cently discovered planets with its minimum mass of 5.6 M⊕ andorbital period of 13.9 d (see Affer et al. 2019).

The radial-velocity signal of the planet is hidden amongstrong signals of various time scales induced by the rotating hoststar and its evolving surface elements. Recently, such cases havebeen discovered quite often in the search for exoplanets orbitingearly M-type stars (e.g., Affer et al. 2016; Perger et al. 2017b),which shows the importance of the understanding of the stellarcontribution to all different kinds of observed time-series data.Whereas there are no signs of differential rotation on the stel-lar surface of Gl 49, the activity level varies significantly overthe observed timespan. This is reflected in a long-term variation,which we suspect to be connected to the motion and reduction ofthe number of dark spots. We do not have sufficient data at hand,but if this behavior were periodic as in the magnetic cycle on theSun, we would estimate its period to be >1500 d, which is consis-tent with recent findings for early M dwarfs (Suárez Mascareño

et al. 2018). We also detect a periodicity of some 40 to 80 d,which we explain with the timescale for the evolution of spotsand faculae. Interestingly, those signals are visible in all our dif-ferent datasets no matter if they are rather measurements of phe-nomena produced by actual motions (CCF), or by stellar activity(lines, photometry), which are rather connected to changes intemperature or pressure. From 2012 to 2018, the star went froman unusual season at the beginning up to a peak of activity in2013–2014. After passing a local minimum in 2015, the level ofactivity rose to a less-active local maximum in 2016–2017 andis since then again slowly losing strength. In the framework ofthe CARMENES M-dwarf program, Gl 49 will be observed inthe up-coming semesters in order to monitor this interesting be-havior in all the different time-series data.Acknowledgements. M.P., I.R., J.C.M, D.B., E.H, and M.L., acknowledge sup-port from the Spanish Ministry of Economy and Competitiveness (MINECO)and the Fondo Europeo de Desarrollo Regional (FEDER) through grantESP2016-80435-C2-1-R, as well as the support of the Generalitat deCatalunya/CERCA program. G.S. acknowledges financial support from “Ac-cordo ASI–INAF” No. 2013-016-R.0 July 9, 2013 and July 9, 2015. The ItalianTelescopio Nazionale Galileo (TNG) is operated on the island of La Palma bythe INAF - Fundación Galileo Galilei at the Roque de los Muchachos Obser-vatory of the Instituto de Astrofísica de Canarias (IAC). The HARPS-N Projectis a collaboration between the Astronomical Observatory of the Geneva Uni-versity (lead), the CfA in Cambridge, the Universities of St. Andrews and Ed-inburgh, the Queen’s University of Belfast, and the TNG-INAF Observatory.CARMENES is an instrument for the Centro Astronómico Hispano-Alemán deCalar Alto (CAHA, Almería, Spain). CARMENES is funded by the GermanMax-Planck-Gesellschaft (MPG), the Spanish Consejo Superior de Investiga-ciones Científicas (CSIC), the European Union through FEDER/ERF FICTS-2011-02 funds, and the members of the CARMENES Consortium (Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Andalucía, LandessternwarteKönigstuhl, Institut de Ciències de l’Espai, Insitut für Astrophysik Göttingen,Universidad Complutense de Madrid, Thüringer Landessternwarte Tautenburg,Instituto de Astrofísica de Canarias, Hamburger Sternwarte, Centro de Astro-biología and Centro Astronómico Hispano-Alemán), with additional contribu-tions by the Spanish Ministry of Science through projects RYC2013-14875,AYA2015-69350-C3-2-P, AYA2016-79425-C3-1/2/3-P, ESP2016-80435-C2-1-R, ESP2017-87143-R, ESP2017-87676-C05-1/2/5-R, and AYA2017-86389-P,the German Science Foundation through the Major Research Instrumenta-tion Program and DFG Research Unit FOR2544 “Blue Planets around RedStars”, the Klaus Tschira Stiftung, the states of Baden-Württemberg andNiedersachsen, and by the Junta de Andalucía. Additional support was pro-vided by the European Union FP7/2007-2013 program under grant agree-ment No. 313014 (ETAEARTH), the Progetto Premiale INAF 2015 FRON-TIER, and the "Center of Excellence Severo Ochoa" award for the Institutode Astrofísica de Andalucía (SEV-2017-0709). This work has made use ofdata from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Anal-ysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions,in particular the institutions participating in the Gaia Multilateral Agreement.Gl 49b wurde für Sibel entdeckt.

ReferencesAffer, L., Damasso, M., Micela, G., et al. 2019, A&A, 622, A193Affer, L., Micela, G., Damasso, M., et al. 2016, A&A, 593, A117Alonso-Floriano, F. J., Morales, J. C., Caballero, J. A., et al. 2015, A&A, 577,

A128Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., & O’Neil,

M. 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence,38

Anglada-Escudé, G. & Butler, R. P. 2012, ApJS, 200, 15Anglada-Escudé, G., Tuomi, M., Gerlach, E., et al. 2013, A&A, 556, A126Baluev, R. V. 2013, MNRAS, 429, 2052Berdiñas, Z. M., Amado, P. J., Anglada-Escudé, G., Rodríguez-López, C., &

Barnes, J. 2016, MNRAS, 459, 3551Berta, Z. K., Irwin, J., Charbonneau, D., Burke, C. J., & Falco, E. E. 2012, AJ,

144, 145Boller, T., Freyberg, M. J., Trümper, J., et al. 2016, A&A, 588, A103Bonfils, X., Delfosse, X., Udry, S., et al. 2013, A&A, 549, A109Bradshaw, S. J. & Hartigan, P. 2014, ApJ, 795, 79


https://www.cosmos.esa.int/gaia

https://www.cosmos.esa.int/gaia

https://www.cosmos.esa.int/web/gaia/dpac/consortium

https://www.cosmos.esa.int/web/gaia/dpac/consortium


Broeg, C., Benz, W., Thomas, N., & Cheops Team. 2014, Contributions of theAstronomical Observatory Skalnate Pleso, 43, 498

Butler, R. P., Vogt, S. S., Laughlin, G., et al. 2017, AJ, 153, 208Caballero, J. A., Cortés-Contreras, M., Alonso-Floriano, F. J., et al. 2016a,

in 19th Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun(CS19), 148

Caballero, J. A., Guàrdia, J., López del Fresno, M., et al. 2016b, in Proc. SPIE,Vol. 9910, Observatory Operations: Strategies, Processes, and Systems VI,99100E

Cortés-Contreras, M. 2016, PhD thesis, Universidad Complutense de Madrid,Spain

Cosentino, R., Lovis, C., Pepe, F., et al. 2012, in Society of Photo-Optical Instru-mentation Engineers (SPIE) Conference Series, Vol. 8446, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 1

Davenport, J. R. A., Hebb, L., & Hawley, S. L. 2015, ApJ, 806, 212Díaz, R. F., Cincunegui, C., & Mauas, P. J. D. 2007, MNRAS, 378, 1007Díez Alonso, E., Caballero, J. A., Montes, D., et al. 2019, A&A, 621, A126Dressing, C. D. & Charbonneau, D. 2013, ApJ, 767, 95Dumusque, X., Bonomo, A. S., Haywood, R. D., et al. 2014, ApJ, 789, 154Dumusque, X., Udry, S., Lovis, C., Santos, N. C., & Monteiro, M. J. P. F. G.

2011, A&A, 525, A140Duncan, D. K., Vaughan, A. H., Wilson, O. C., et al. 1991, ApJS, 76, 383Feng, F., Tuomi, M., Jones, H. R. A., Butler, R. P., & Vogt, S. 2016, MNRAS,

461, 2440Foreman-Mackey, D., Agol, E., Ambikasaran, S., & Angus, R. 2017, AJ, 154,

220Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125,

306Gagné, J., Faherty, J. K., Cruz, K. L., et al. 2015, ApJS, 219, 33Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1Giles, H. A. C., Collier Cameron, A., & Haywood, R. D. 2017, MNRAS, 472,

1618Gomes da Silva, J., Santos, N. C., Bonfils, X., et al. 2011, A&A, 534, A30González-Álvarez, E., Micela, G., Maldonado, J., et al. 2019, arXiv e-printsHøg, E., Kuzmin, A., Bastian, U., et al. 1998, A&A, 335, L65Horne, J. H. & Baliunas, S. L. 1986, ApJ, 302, 757Jeffers, S. V., Schöfer, P., Lamert, A., et al. 2018, A&A, 614, A76Kaminski, A., Trifonov, T., Caballero, J. A., et al. 2018, A&A, 618, A115Kiraga, M. & Stepien, K. 2007, Acta Astron., 57, 149Kopparapu, R. K., Ramirez, R., Kasting, J. F., et al. 2013, ApJ, 765, 131Lovis, C., Dumusque, X., Santos, N. C., et al. 2011, ArXiv, 11075325Lovis, C. & Pepe, F. 2007, A&A, 468, 1115Luque, R., Nowak, G., Pallé, E., et al. 2018, A&A, 620, A171Maldonado, J., Scandariato, G., Stelzer, B., et al. 2017, A&A, 598, A27Mason, B. D., Wycoff, G. L., Hartkopf, W. I., Douglass, G. G., & Worley, C. E.

2001, AJ, 122, 3466Mauas, P. J. D. 2000, ApJ, 539, 858Mayor, M., Marmier, M., Lovis, C., et al. 2011, ArXiv, 11092497Mayor, M., Pepe, F., Queloz, D., et al. 2003, The Messenger, 114, 20Murdoch, K. A., Hearnshaw, J. B., & Clark, M. 1993, ApJ, 413, 349Nagel, E., Czesla, S., Schmitt, J. H. M. M., et al. 2019, A&A, 622, A153Newton, E. R., Irwin, J., Charbonneau, D., Berta-Thompson, Z. K., & Dittmann,

J. A. 2016, ApJ, 821, L19Perger, M., García-Piquer, A., Ribas, I., et al. 2017a, A&A, 598, A26Perger, M., Ribas, I., Damasso, M., et al. 2017b, A&A, 608, A63Petrovay, K. & van Driel-Gesztelyi, L. 1997, Sol. Phys., 176, 249Pinamonti, M., Damasso, M., Marzari, F., et al. 2018, A&A, 617, A104Pojmanski, G. 1997, Acta Astron., 47, 467Queloz, D., Henry, G. W., Sivan, J. P., et al. 2001, A&A, 379, 279Quirrenbach, A., Amado, P. J., Caballero, J. A., et al. 2016, in Proc. SPIE,

Vol. 9908, Ground-based and Airborne Instrumentation for Astronomy VI,990812

Quirrenbach, A., Amado, P. J., Ribas, I., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, So-ciety of Photo-Optical Instrumentation Engineers (SPIE) Conference Series,107020W

Rajpaul, V., Aigrain, S., Osborne, M. A., Reece, S., & Roberts, S. 2015, MN-RAS, 452, 2269

Reiners, A., Ribas, I., Zechmeister, M., et al. 2018a, A&A, 609, L5Reiners, A., Zechmeister, M., Caballero, J. A., et al. 2018b, A&A, 612, A49Ribas, I., Tuomi, M., Reiners, A., et al. 2018, Nature, 563, 365Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, Journal of Astronomical

Telescopes, Instruments, and Systems, 1, 014003Roberts, S., Osborne, M., Ebden, M., et al. 2013, Philosophical Transactions

of the Royal Society of London A: Mathematical, Physical and EngineeringSciences, 371

Rodríguez, E., García, J. M., Costa, V., et al. 2010, MNRAS, 408, 2149Sarkis, P., Henning, T., Kürster, M., et al. 2018, AJ, 155, 257Scandariato, G., Maldonado, J., Affer, L., et al. 2017, A&A, 598, A28Schmitt, J. H. M. M., Fleming, T. A., & Giampapa, M. S. 1995, ApJ, 450, 392

Schöfer, P., Jeffers, S. V., Reiners, A., et al. 2019, A&A, 623, A44Schweitzer et al., A. 2019, A&A, submittedSkrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163Strassmeier, K. G. 2009, A&A Rev., 17, 251Suárez Mascareño, A., González Hernández, J. I., Rebolo, R., et al. 2017a, A&A,

605, A92Suárez Mascareño, A., Rebolo, R., & González Hernández, J. I. 2016, A&A,

595, A12Suárez Mascareño, A., Rebolo, R., González Hernández, J. I., & Esposito, M.

2017b, MNRAS, 468, 4772Suárez Mascareño, A., Rebolo, R., González Hernández, J. I., et al. 2018, A&A,

612, A89Tal-Or, L., Trifonov, T., Zucker, S., Mazeh, T., & Zechmeister, M. 2019, MN-

RAS, 484, L8Tal-Or, L., Zechmeister, M., Reiners, A., et al. 2018, A&A, 614, A122Testa, P., Saar, S. H., & Drake, J. J. 2015, Philosophical Transactions of the Royal

Society of London Series A, 373, 20140259Trifonov, T., Kürster, M., Zechmeister, M., et al. 2018, A&A, 609, A117Vogt, S. S., Allen, S. L., Bigelow, B. C., et al. 1994, in Proc. SPIE, Vol. 2198,

Instrumentation in Astronomy VIII, ed. D. L. Crawford & E. R. Craine, 362Wright, J. T. & Eastman, J. D. 2014, PASP, 126, 838Zechmeister, M. & Kürster, M. 2009, A&A, 496, 577Zechmeister, M., Reiners, A., Amado, P. J., et al. 2018, A&A, 609, A12

1Institut de Ciencies de l’Espai (ICE, CSIC), Campus UAB, Carrer deCan Magrans s/n, 08193 Bellaterra, Spain2Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona,Spain3INAF - Osservatorio Astrofisico di Catania, via S. Sofia 78, 95123Catania, Italy4INAF - Osservatorio Astronomico di Palermo, Piazza del Parlamento1, 90134 Palermo, Italy5Centro Astronómico Hispano-Alemán (CAHA). Calar Alto Observa-tory, c/ Jesús Durbán Remón 2-2, 04004 Almería, Spain6Instituto de Astrofísica de Andalucía (IAA-CSIC), Glorieta de laAstronomía s/n, 18008 Granada, Spain7School of Physics and Astronomy, Queen Mary University of London,327 Mile End Rd, E1 4NS London, United Kingdom8Centro de Astrobiología (CSIC-INTA), Camino Bajo del Castillo s/n,ESAC Campus, 28692, Villanueva, Madrid, Spain9Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, Tenerife,Spain10Universidad de La Laguna (ULL), Departamento de Astrofísica,38206 La Laguna, Tenerife, Spain11INAF - Osservatorio Astrofisico di Torino, via Osservatorio 20,10025 Pino Torinese, Italy12Institut für Astrophysik - Georg-August-Universität Göttingen,Friedrich-Hund-Platz 1, 37077 Göttingen, Germany13Thüringer Landessternwarte Tautenburg, Sternwarte 5, 07778 Taut-enburg, Germany14Max-Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidel-berg, Germany15Landessternwarte, Zentrum für Astronomie der Universtät Heidel-berg, Königstuhl 12, 69117 Heidelberg, Germany16Departamento de Física de la Tierra y Astrofísica & UPARCOS-UCM, Facultad de Ciencias Físicas, Universidad Complutense deMadrid, 28040 Madrid, Spain17Consejo Superior de Investigaciones Científicas (CSIC), 28006Madrid, Spain18Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112,21029 Hamburg, Germany19Observatoire Astronomique de l’Université de Geneve, 1290 Versoix,Switzerland20Centro de Astrobiología (CSIC-INTA), Carretera de Ajalvir km 4,28850 Torrejón de Ardoz, Madrid, Spain

Appendix A: Observational log, data distributions,periodograms, and MCMC solutions


M. Perger et al.: Gliese 49: Activity evolution and detection of a super-EarthTa

ble

A.1

.Rad

ialv

eloc

ityan

dac

tivity

indi

cato

rtim

e-se

ries

data

ofG

l49

from

HIR

ES,

HA

RPS

-N,a

ndC

AR

ME

NE

Sin

stru

men

ts.

BJD

RVC

aHK

/CaI

RT

Hα

Nai

FWH

MC

ON

BIS

CR

Xin

stru

men

t[d

]-24

5000

0[m

s−1 ]

[km

s−1 ]

[%]

[ms−

1 ][m

s−1

N−

1p

]an

dse

ason

667.

0532

0.81

10±

1.33

00..

...

...

...

...

...

...

.H

IRE

SS0

716.

0104

-0.8

290±

1.45

00..

...

...

...

...

...

...

.H

IRE

SS0

1044

.081

43.

3310±

1.15

00..

...

...

...

...

...

...

.H

IRE

SS0

1071

.038

6-3

.689

0±1.

1900

...

...

...

...

...

...

...

HIR

ES

S011

73.8

468

-8.1

490±

1.29

00..

...

...

...

...

...

...

.H

IRE

SS0

1368

.058

43.

5710±

1.58

00..

...

...

...

...

...

...

.H

IRE

SS0

1543

.787

28.

0210±

1.29

00..

...

...

...

...

...

...

.H

IRE

SS0

1757

.093

5-5

.889

0±1.

3100

...

...

...

...

...

...

...

HIR

ES

S018

99.8

454

-0.3

390±

1.53

00..

...

...

...

...

...

...

.H

IRE

SS0

2133

.089

7-1

.779

0±1.

2400

...

...

...

...

...

...

...

HIR

ES

S021

33.9

996

2.59

10±

1.38

00..

...

...

...

...

...

...

.H

IRE

SS0

2242

.885

1-5

.769

0±1.

4000

...

...

...

...

...

...

...

HIR

ES

S025

15.0

599

-3.2

090±

1.39

00..

...

...

...

...

...

...

.H

IRE

SS0

2535

.982

4-4

.529

0±1.

2800

...

...

...

...

...

...

...

HIR

ES

S028

34.0

661

1.83

10±

1.33

00..

...

...

...

...

...

...

.H

IRE

SS0

3197

.113

0-0

.159

0±1.

6200

...

...

...

...

...

...

...

HIR

ES

S054

27.0

842

6.23

10±

1.02

00..

...

...

...

...

...

...

.H

IRE

SS0

5516

.019

88.

0710±

1.19

00..

...

...

...

...

...

...

.H

IRE

SS0

5517

.943

29.

5310±

1.09

00..

...

...

...

...

...

...

.H

IRE

SS0

5522

.983

8-4

.469

0±1.

2800

...

...

...

...

...

...

...

HIR

ES

S058

51.9

753

-5.1

790±

1.24

00..

...

...

...

...

...

...

.H

IRE

SS0

6173

.735

9-1

.775

0±0.

8992

4.13

98±

0.00

890.

8946±

0.00

250.

5641±

0.00

353.

229±

0.01

420

.114±

0.08

1-8

.60±

0.43

...

HA

RPS

-NS1

6176

.668

0-0

.339

1±1.

4275

3.47

71±

0.01

100.

8555±

0.00

270.

5660±

0.00

353.

355±

0.01

321

.718±

0.08

2-2

.07±

0.65

...

HA

RPS

-NS1

6177

.701

7-1

.561

1±1.

2475

3.69

67±

0.00

880.

8647±

0.00

270.

5653±

0.00

353.

286±

0.01

320

.639±

0.07

80.

94±

0.48

...

HA

RPS

-NS1

6178

.663

53.

3061±

1.26

743.

4641±

0.00

870.

8507±

0.00

280.

5657±

0.00

353.

277±

0.01

320

.562±

0.08

0-1

.22±

0.49

...

HA

RPS

-NS1

6178

.732

74.

0106±

1.30

563.

6024±

0.00

910.

8599±

0.00

270.

5675±

0.00

353.

257±

0.01

320

.318±

0.07

7-2

.87±

0.48

...

HA

RPS

-NS1

6181

.593

217

.322

4±1.

2894

4.07

82±

0.00

960.

8854±

0.00

260.

5693±

0.00

353.

229±

0.01

419

.938±

0.07

9-9

.36±

0.45

...

HA

RPS

-NS1

6244

.552

9-1

0.09

90±

1.52

074.

4908±

0.01

710.

9792±

0.00

210.

5767±

0.00

343.

276±

0.01

320

.167±

0.07

8-6

.09±

0.74

...

HA

RPS

-NS1

6255

.434

411

.607

0±1.

3527

3.86

05±

0.01

450.

9420±

0.00

230.

5849±

0.00

333.

317±

0.01

320

.838±

0.08

1-6

.70±

0.77

...

HA

RPS

-NS1

6255

.522

311

.955

6±1.

2206

3.85

01±

0.00

990.

9306±

0.00

230.

5700±

0.00

353.

231±

0.01

419

.857±

0.07

8-5

.96±

0.44

...

HA

RPS

-NS1

6260

.483

0-1

.029

1±2.

3740

4.18

00±

0.03

030.

9880±

0.00

200.

5851±

0.00

333.

236±

0.01

419

.938±

0.08

0-2

.18±

2.18

...

HA

RPS

-NS1

6260

.534

61.

3631±

1.97

234.

3138±

0.03

250.

9919±

0.00

200.

5911±

0.00

323.

242±

0.01

419

.915±

0.08

61.

88±

2.27

...

HA

RPS

-NS1

6295

.326

617

.426

8±1.

3872

4.61

15±

0.01

231.

0166±

0.00

190.

5803±

0.00

333.

286±

0.01

320

.303±

0.07

6-8

.20±

0.56

...

HA

RPS

-NS1

6295

.424

016

.672

0±1.

2901

5.26

17±

0.01

311.

0734±

0.00

170.

5854±

0.00

323.

282±

0.01

320

.208±

0.07

9-9

.41±

0.53

...

HA

RPS

-NS1

6296

.411

113

.186

7±1.

1628

4.84

76±

0.01

071.

0567±

0.00

180.

5811±

0.00

333.

302±

0.01

320

.287±

0.08

0-6

.94±

0.45

...

HA

RPS

-NS1

6296

.447

212

.811

9±1.

2740

4.64

09±

0.01

141.

0369±

0.00

190.

5804±

0.00

333.

319±

0.01

320

.579±

0.08

0-6

.68±

0.51

...

HA

RPS

-NS1

6297

.357

26.

0887±

1.17

004.

4540±

0.00

871.

0004±

0.00

200.

5746±

0.00

343.

331±

0.01

320

.558±

0.08

0-6

.38±

0.42

...

HA

RPS

-NS1

6297

.409

53.

9509±

1.22

594.

7919±

0.01

271.

0269±

0.00

190.

5795±

0.00

333.

321±

0.01

320

.593±

0.08

0-9

.76±

0.56

...

HA

RPS

-NS1

6298

.358

1-5

.039

7±1.

2660

5.00

60±

0.01

041.

0456±

0.00

180.

5822±

0.00

333.

348±

0.01

320

.827±

0.07

7-1

.70±

0.48

...

HA

RPS

-NS1

6298

.408

0-6

.815

6±1.

1938

4.67

89±

0.01

051.

0159±

0.00

190.

5764±

0.00

343.

318±

0.01

320

.449±

0.07

6-5

.68±

0.48

...

HA

RPS

-NS1

6299

.382

6-1

0.72

92±

1.17

564.

6175±

0.00

891.

0194±

0.00

190.

5762±

0.00

343.

323±

0.01

320

.434±

0.07

7-3

.09±

0.42

...

HA

RPS

-NS1

6305

.372

0-5

.187

7±1.

2125

3.56

79±

0.00

860.

9154±

0.00

240.

5672±

0.00

353.

326±

0.01

320

.884±

0.07

9-5

.87±

0.46

...

HA

RPS

-NS1

6322

.388

8-1

2.95

77±

1.21

584.

2915±

0.00

950.

9772±

0.00

210.

5753±

0.00

343.

292±

0.01

320

.256±

0.07

8-3

.15±

0.44

...

HA

RPS

-NS1

6322

.452

8-1

5.35

83±

1.18

574.

1317±

0.01

160.

9633±

0.00

220.

5741±

0.00

343.

296±

0.01

320

.369±

0.07

8-2

.62±

0.51

...

HA

RPS

-NS1

6323

.332

9-1

1.88

10±

1.19

634.

0113±

0.01

270.

9517±

0.00

220.

5724±

0.00

343.

299±

0.01

320

.554±

0.07

7-3

.01±

0.61

...

HA

RPS

-NS1

6323

.430

0-1

0.88

62±

1.24

304.

0261±

0.01

440.

9599±

0.00

220.

5702±

0.00

343.

278±

0.01

320

.331±

0.07

8-2

.44±

0.64

...

HA

RPS

-NS1

6324

.358

7-9

.470

0±1.

8958

3.86

49±

0.02

680.

9380±

0.00

230.

5771±

0.00

343.

278±

0.01

320

.485±

0.07

41.

06±

1.68

...

HA

RPS

-NS1

6324

.436

0-6

.663

7±1.

4805

3.80

25±

0.02

210.

9484±

0.00

220.

5768±

0.00

343.

285±

0.01

320

.594±

0.08

6-5

.42±

1.21

...

HA

RPS

-NS1

6506

.635

82.

3203±

1.26

184.

5983±

0.01

491.

0055±

0.00

200.

5827±

0.00

333.

327±

0.01

320

.290±

0.07

9-5

.89±

0.65

-12.

1±6.

6H

AR

PS-N

S265

08.6

885

3.37

09±

1.14

254.

7076±

0.00

931.

0147±

0.00

190.

5806±

0.00

333.

336±

0.01

320

.332±

0.07

6-4

.90±

0.41

-0.8±

6.3

HA

RPS

-NS2

6509

.729

0-1

.530

8±1.

1858

4.76

82±

0.01

331.

0151±

0.00

190.

5797±

0.00

343.

294±

0.01

320

.044±

0.07

6-3

.25±

0.54

-7.6±

6.7

HA

RPS

-NS2

6516

.637

7-9

.039

8±1.

2467

4.35

11±

0.01

290.

9829±

0.00

210.

5743±

0.00

343.

310±

0.01

320

.362±

0.07

7-0

.66±

0.56

3.3±

6.6

HA

RPS

-NS2


A&A proofs: manuscript no. Gl49b_finalTa

ble

A.1

.Rad

ialv

eloc

ityan

dac

tivity

indi

cato

rtim

e-se

ries

data

ofG

l49

from

HIR

ES,

HA

RPS

-N,a

ndC

AR

ME

NE

Sin

stru

men

ts(c

ont.)

.

BJD

RVC

aHK

/CaI

RT

Hα

Nai

FWH

MC

ON

BIS

CR

Xin

stru

men

t[d

]-24

5000

0[m

s−1 ]

[km

s−1 ]

[%]

[ms−

1 ][m

s−1

N−

1p

]an

dse

ason

6529

.715

82.

3432±

1.23

574.

7058±

0.01

091.

0116±

0.00

200.

5783±

0.00

343.

263±

0.01

319

.608±

0.08

0-6

.72±

0.44

-35.

1±6.

7H

AR

PS-N

S265

33.5

034

1.22

40±

1.18

334.

7104±

0.01

011.

0221±

0.00

190.

5768±

0.00

343.

362±

0.01

320

.701±

0.07

5-6

.10±

0.45

-9.5±

7.6

HA

RPS

-NS2

6533

.611

62.

2137±

1.20

014.

7227±

0.00

971.

0241±

0.00

190.

5765±

0.00

343.

354±

0.01

320

.678±

0.07

8-4

.55±

0.44

-5.1±

6.8

HA

RPS

-NS2

6534

.561

2-2

.409

3±1.

3222

4.54

51±

0.01

581.

0102±

0.00

200.

5729±

0.00

343.

312±

0.01

320

.325±

0.07

9-3

.15±

0.66

-20.

6±8.

0H

AR

PS-N

S265

35.6

417

-0.8

885±

1.18

814.

7448±

0.00

901.

0310±

0.00

190.

5780±

0.00

343.

356±

0.01

320

.760±

0.07

8-1

.74±

0.41

-9.5±

6.1

HA

RPS

-NS2

6537

.584

7-6

.719

0±1.

1599

4.32

28±

0.01

030.

9839±

0.00

210.

5761±

0.00

343.

293±

0.01

320

.282±

0.07

4-5

.89±

0.46

-13.

9±5.

3H

AR

PS-N

S265

44.6

457

1.06

41±

1.21

104.

8527±

0.01

071.

0238±

0.00

190.

5793±

0.00

333.

344±

0.01

320

.428±

0.07

3-5

.33±

0.46

-6.7±

7.0

HA

RPS

-NS2

6546

.603

90.

1094±

1.16

924.

6964±

0.01

091.

0115±

0.00

200.

5779±

0.00

343.

345±

0.01

320

.435±

0.07

6-3

.24±

0.48

-0.7±

6.0

HA

RPS

-NS2

6579

.729

20.

2502±

1.20

594.

4994±

0.01

060.

9955±

0.00

200.

5745±

0.00

343.

318±

0.01

320

.238±

0.07

9-5

.09±

0.43

-14.

0±7.

5H

AR

PS-N

S265

83.5

733

-0.1

905±

1.09

014.

6355±

0.01

351.

0195±

0.00

190.

5851±

0.00

333.

319±

0.01

320

.146±

0.07

6-2

.82±

0.58

-10.

2±6.

2H

AR

PS-N

S266

04.5

628

5.01

78±

1.27

124.

7568±

0.01

671.

0327±

0.00

190.

5813±

0.00

333.

311±

0.01

320

.111±

0.08

0-7

.38±

0.67

0.5±

8.5

HA

RPS

-NS2

6606

.612

92.

7993±

1.25

654.

3281±

0.01

570.

9854±

0.00

210.

5785±

0.00

333.

356±

0.01

320

.594±

0.07

50.

62±

0.69

-6.5±

8.0

HA

RPS

-NS2

6611

.571

6-1

.627

7±1.

3244

4.36

61±

0.01

640.

9921±

0.00

200.

5760±

0.00

343.

321±

0.01

320

.251±

0.07

4-4

.63±

2.89

-11.

7±7.

5H

AR

PS-N

S266

11.5

841

-3.4

033±

1.33

014.

4899±

0.01

530.

9847±

0.00

210.

5756±

0.00

343.

310±

0.01

320

.157±

0.08

0-2

.02±

2.64

-9.3±

8.4

HA

RPS

-NS2

6617

.507

16.

0244±

1.37

254.

7050±

0.01

611.

0038±

0.00

200.

5755±

0.00

343.

319±

0.01

320

.256±

0.07

9-2

.96±

0.67

-15.

5±9.

0H

AR

PS-N

S266

18.5

505

2.82

44±

1.29

074.

6289±

0.01

040.

9942±

0.00

200.

5741±

0.00

343.

291±

0.01

319

.872±

0.07

9-5

.32±

0.43

-26.

5±6.

0H

AR

PS-N

S266

21.3

900

-0.8

679±

1.19

774.

5393±

0.01

090.

9875±

0.00

210.

5739±

0.00

343.

295±

0.01

319

.931±

0.07

8-8

.12±

0.46

-10.

6±6.

7H

AR

PS-N

S266

25.5

642

6.20

12±

1.38

415.

3185±

0.02

101.

0799±

0.00

170.

5893±

0.00

323.

323±

0.01

320

.257±

0.08

2-4

.22±

3.26

-14.

2±9.

7H

AR

PS-N

S266

25.5

748

5.05

47±

1.30

925.

3753±

0.01

981.

0685±

0.00

180.

5869±

0.00

323.

327±

0.01

320

.243±

0.07

8-5

.24±

3.04

-2.2±

8.2

HA

RPS

-NS2

6854

.664

31.

0187±

1.22

464.

1574±

0.01

140.

9685±

0.00

210.

5714±

0.00

343.

277±

0.01

320

.864±

0.07

9-2

.76±

0.47

0.9±

7.7

HA

RPS

-NS3

6855

.687

41.

0357±

1.23

244.

1842±

0.01

370.

9728±

0.00

210.

5735±

0.00

343.

267±

0.01

320

.870±

0.08

3-5

.76±

0.55

-0.9±

7.7

HA

RPS

-NS3

6879

.677

61.

8299±

1.21

494.

4878±

0.01

191.

0058±

0.00

200.

5785±

0.00

343.

300±

0.01

321

.260±

0.08

2-3

.04±

0.48

-7.9±

6.9

HA

RPS

-NS3

6880

.669

4-2

.016

2±1.

2035

4.19

42±

0.01

460.

9813±

0.00

210.

5753±

0.00

343.

287±

0.01

321

.201±

0.08

2-1

.26±

0.58

7.0±

6.3

HA

RPS

-NS3

6881

.611

3-1

.517

4±1.

2451

3.97

77±

0.01

380.

9587±

0.00

220.

5706±

0.00

353.

331±

0.01

321

.797±

0.08

4-2

.25±

0.61

20.3±

6.8

HA

RPS

-NS3

6892

.649

20.

0351±

1.15

244.

0618±

0.01

020.

9650±

0.00

210.

5765±

0.00

343.

287±

0.01

321

.087±

0.07

9-5

.45±

0.44

-2.1±

5.6

HA

RPS

-NS3

6893

.627

33.

6747±

1.26

404.

2764±

0.01

450.

9901±

0.00

200.

5782±

0.00

343.

297±

0.01

321

.355±

0.08

2-4

.28±

0.60

3.3±

7.3

HA

RPS

-NS3

6894

.646

99.

1049±

1.31

124.

2697±

0.01

740.

9957±

0.00

200.

5781±

0.00

333.

271±

0.01

321

.034±

0.08

4-5

.59±

0.71

0.5±

8.7

HA

RPS

-NS3

6897

.641

03.

7368±

0.80

174.

1894±

0.01

200.

9732±

0.00

210.

5781±

0.00

343.

333±

0.01

321

.763±

0.08

0-2

.19±

0.53

19.7±

6.5

HA

RPS

-NS3

6898

.624

9-1

.073

1±0.

6758

4.22

48±

0.01

070.

9725±

0.00

210.

5742±

0.00

343.

312±

0.01

321

.453±

0.08

0-3

.05±

0.47

-4.1±

6.7

HA

RPS

-NS3

6899

.632

7-3

.519

7±0.

6746

4.15

57±

0.01

190.

9643±

0.00

220.

5751±

0.00

343.

298±

0.01

321

.317±

0.08

4-2

.87±

0.51

9.5±

7.3

HA

RPS

-NS3

6903

.659

8-2

.560

3±0.

6348

4.13

28±

0.00

870.

9531±

0.00

220.

5706±

0.00

353.

297±

0.01

321

.423±

0.08

2-3

.74±

0.38

11.1±

6.9

HA

RPS

-NS3

6904

.662

2-2

.227

3±0.

6020

4.16

67±

0.01

140.

9486±

0.00

220.

5707±

0.00

353.

229±

0.01

420

.627±

0.08

2-6

.66±

0.45

-8.5±

5.7

HA

RPS

-NS3

6905

.638

12.

1950±

0.79

704.

1096±

0.01

170.

9510±

0.00

220.

5701±

0.00

353.

296±

0.01

321

.494±

0.08

2-3

.73±

0.50

21.2±

8.8

HA

RPS

-NS3

6918

.706

9-1

.600

8±0.

9263

4.45

55±

0.01

620.

9760±

0.00

210.

5733±

0.00

343.

265±

0.01

321

.032±

0.08

3-3

.26±

0.62

0.1±

8.1

HA

RPS

-NS3

6919

.648

0-1

.854

4±0.

7472

4.16

28±

0.01

260.

9571±

0.00

220.

5726±

0.00

343.

298±

0.01

321

.432±

0.08

0-6

.63±

0.54

-1.7±

6.7

HA

RPS

-NS3

6920

.674

6-1

.003

3±0.

5990

4.19

49±

0.01

390.

9666±

0.00

210.

5761±

0.00

343.

262±

0.01

320

.987±

0.07

9-6

.64±

0.57

1.6±

6.4

HA

RPS

-NS3

6928

.702

90.

7874±

1.00

554.

2716±

0.01

650.

9658±

0.00

210.

5748±

0.00

343.

172±

0.01

420

.086±

0.08

3-5

.32±

0.57

-22.

1±8.

2H

AR

PS-N

S369

29.7

220

-4.3

450±

0.75

194.

3170±

0.01

330.

9757±

0.00

210.

5746±

0.00

343.

212±

0.01

420

.336±

0.08

1-1

.69±

0.47

-4.0±

6.3

HA

RPS

-NS3

6930

.704

9-5

.270

5±0.

7044

4.34

34±

0.01

140.

9907±

0.00

200.

5775±

0.00

343.

277±

0.01

321

.074±

0.08

2-4

.52±

0.46

5.2±

6.6

HA

RPS

-NS3

6931

.723

30.

9824±

0.48

214.

3169±

0.00

950.

9830±

0.00

210.

5749±

0.00

343.

286±

0.01

321

.170±

0.08

2-2

.78±

0.40

8.2±

4.7

HA

RPS

-NS3

6932

.682

4-0

.465

4±0.

8408

4.19

17±

0.01

390.

9641±

0.00

220.

5715±

0.00

343.

287±

0.01

321

.265±

0.08

2-2

.38±

0.56

23.0±

7.5

HA

RPS

-NS3

6937

.648

21.

0489±

0.56

315.

1195±

0.01

041.

0510±

0.00

180.

5825±

0.00

333.

268±

0.01

320

.872±

0.08

0-6

.18±

0.38

8.5±

5.7

HA

RPS

-NS3

6938

.652

1-0

.421

4±0.

8151

4.30

81±

0.01

230.

9806±

0.00

210.

5736±

0.00

343.

237±

0.01

420

.598±

0.08

5-2

.64±

0.48

10.2±

7.4

HA

RPS

-NS3

6939

.687

3-1

.547

9±1.

3899

3.95

46±

0.02

400.

9644±

0.00

220.

6005±

0.00

313.

320±

0.01

321

.432±

0.08

3-0

.54±

1.41

1.8±

12.5

HA

RPS

-NS3

6940

.644

9-4

.994

0±0.

6380

4.26

67±

0.01

150.

9755±

0.00

210.

5756±

0.00

343.

294±

0.01

321

.339±

0.08

5-3

.96±

0.47

2.6±

5.5

HA

RPS

-NS3

6943

.651

20.

1842±

0.76

684.

0988±

0.01

280.

9528±

0.00

220.

5702±

0.00

343.

255±

0.01

320

.962±

0.08

5-3

.51±

0.52

0.4±

7.2

HA

RPS

-NS3

7239

.622

4-0

.346

6±0.

6098

4.15

58±

0.01

120.

9505±

0.00

220.

5707±

0.00

353.

258±

0.01

320

.975±

0.07

7-5

.46±

0.45

0.1±

6.3

HA

RPS

-NS4

7240

.622

9-1

.893

5±0.

7604

4.19

07±

0.01

300.

9552±

0.00

220.

5740±

0.00

343.

278±

0.01

321

.238±

0.08

1-4

.00±

0.52

19.5±

7.3

HA

RPS

-NS4

7241

.628

50.

3910±

0.62

584.

0443±

0.01

370.

9480±

0.00

220.

5729±

0.00

343.

272±

0.01

321

.240±

0.08

1-3

.80±

0.56

8.4±

7.3

HA

RPS

-NS4

7242

.698

15.

1989±

2.95

595.

6241±

0.05

870.

9627±

0.00

220.

5999±

0.00

313.

257±

0.01

321

.005±

0.07

1-6

.75±

3.06

22.7±

19.2

HA

RPS

-NS4

7251

.676

44.

5748±

0.71

464.

0754±

0.01

180.

9451±

0.00

220.

5719±

0.00

343.

267±

0.01

320

.976±

0.08

2-3

.51±

0.49

5.4±

7.2

HA

RPS

-NS4

7260

.604

1-3

.149

8±0.

8176

4.28

01±

0.01

280.

9699±

0.00

210.

5736±

0.00

343.

285±

0.01

321

.383±

0.08

4-1

.72±

0.53

9.1±

7.2

HA

RPS

-NS4

7261

.623

4-3

.131

1±0.

7851

4.21

75±

0.01

430.

9667±

0.00

210.

5724±

0.00

343.

237±

0.01

420

.861±

0.08

1-2

.69±

0.57

1.9±

8.2

HA

RPS

-NS4



ble

A.1

.Rad

ialv

eloc

ityan

dac

tivity

indi

cato

rtim

e-se

ries

data

ofG

l49

from

HIR

ES,

HA

RPS

-N,a

ndC

AR

ME

NE

Sin

stru

men

ts(c

ont.)

.

BJD

RVC

aHK

/CaI

RT

Hα

Nai

FWH

MC

ON

BIS

CR

Xin

stru

men

t[d

]-24

5000

0[m

s−1 ]

[km

s−1 ]

[%]

[ms−

1 ][m

s−1

N−

1p

]an

dse

ason

7263

.596

9-1

.988

1±0.

9299

4.24

93±

0.01

440.

9805±

0.00

210.

5799±

0.00

343.

309±

0.01

321

.661±

0.08

0-6

.21±

0.62

5.6±

8.6

HA

RPS

-NS4

7264

.595

20.

6718±

0.87

293.

9212±

0.01

350.

9423±

0.00

230.

5753±

0.00

343.

294±

0.01

321

.479±

0.08

1-4

.96±

0.59

18.9±

8.4

HA

RPS

-NS4

7269

.604

77.

1802±

1.22

894.

4411±

0.01

260.

9848±

0.00

210.

5799±

0.00

333.

254±

0.01

320

.737±

0.08

3-3

.24±

0.49

9.7±

7.9

HA

RPS

-NS4

7270

.588

04.

0779±

1.17

724.

1420±

0.00

980.

9603±

0.00

220.

5746±

0.00

343.

274±

0.01

320

.970±

0.08

3-5

.45±

0.41

10.8±

6.4

HA

RPS

-NS4

7271

.622

96.

9219±

1.24

724.

0244±

0.01

070.

9519±

0.00

220.

5746±

0.00

343.

241±

0.01

420

.633±

0.08

5-5

.73±

0.43

4.7±

6.5

HA

RPS

-NS4

7272

.631

98.

2715±

1.31

634.

1495±

0.01

290.

9558±

0.00

220.

5754±

0.00

343.

240±

0.01

420

.692±

0.08

0-7

.72±

0.52

-4.4±

8.2

HA

RPS

-NS4

7274

.583

31.

8002±

0.78

704.

2728±

0.01

420.

9640±

0.00

220.

5749±

0.00

343.

256±

0.01

320

.960±

0.08

1-4

.69±

0.56

11.2±

7.2

HA

RPS

-NS4

7275

.579

7-1

.438

5±1.

2342

4.22

73±

0.01

340.

9585±

0.00

220.

5745±

0.00

343.

277±

0.01

321

.194±

0.08

1-4

.38±

0.56

5.6±

7.7

HA

RPS

-NS4

7276

.581

9-1

.057

3±0.

5538

4.50

12±

0.01

150.

9880±

0.00

200.

5807±

0.00

333.

278±

0.01

321

.037±

0.08

3-2

.14±

0.48

-17.

7±6.

5H

AR

PS-N

S472

77.5

762

-1.9

999±

0.64

684.

3553±

0.01

260.

9762±

0.00

210.

5766±

0.00

343.

245±

0.01

420

.691±

0.07

9-1

.68±

0.50

1.7±

6.3

HA

RPS

-NS4

7278

.601

6-1

.162

0±0.

6489

4.10

69±

0.01

220.

9603±

0.00

220.

5740±

0.00

343.

269±

0.01

321

.079±

0.08

4-5

.41±

0.51

1.1±

6.2

HA

RPS

-NS4

7282

.596

31.

5189±

0.79

174.

0596±

0.00

920.

9542±

0.00

220.

5739±

0.00

343.

295±

0.01

321

.364±

0.08

2-2

.08±

0.41

-14.

4±20

.9H

AR

PS-N

S472

86.7

258

-2.8

379±

0.76

953.

7668±

0.01

200.

9332±

0.00

230.

5723±

0.00

343.

275±

0.01

321

.137±

0.08

6-5

.39±

0.53

4.9±

8.0

HA

RPS

-NS4

7287

.727

9-2

.385

3±0.

7732

3.74

96±

0.01

510.

9257±

0.00

230.

5747±

0.00

343.

270±

0.01

321

.142±

0.08

1-3

.30±

0.66

15.6±

7.2

HA

RPS

-NS4

7303

.593

0-7

.460

1±0.

7978

4.05

89±

0.01

160.

9624±

0.00

220.

5731±

0.00

343.

312±

0.01

321

.503±

0.08

4-3

.63±

0.50

-5.1±

8.1

HA

RPS

-NS4

7594

.644

7-2

.408

3±1.

2137

3.79

58±

0.01

230.

9499±

0.00

220.

5751±

0.00

343.

242±

0.01

420

.901±

0.08

4-6

.56±

0.53

7.3±

5.9

HA

RPS

-NS5

7603

.655

2-9

.234

9±1.

2514

4.28

16±

0.01

170.

9881±

0.00

200.

5732±

0.00

343.

221±

0.01

420

.713±

0.08

2-6

.33±

0.48

4.3±

7.3

HA

RPS

-NS5

7604

.647

5-8

.570

5±1.

6629

3.67

79±

0.02

590.

9565±

0.00

220.

5771±

0.00

343.

252±

0.01

421

.208±

0.09

4-1

0.97±

1.56

-6.7±

13.0

HA

RPS

-NS5

7620

.556

6-1

.906

1±1.

5985

4.24

21±

0.01

960.

9975±

0.00

200.

5788±

0.00

343.

253±

0.01

421

.183±

0.08

1-0

.73±

0.84

5.1±

11.7

HA

RPS

-NS5

7624

.665

2-2

.552

4±1.

2270

3.71

67±

0.01

040.

9433±

0.00

220.

5697±

0.00

353.

240±

0.01

421

.028±

0.08

2-2

.46±

0.48

-1.1±

6.8

HA

RPS

-NS5

7644

.665

91.

4244±

1.30

183.

8637±

0.01

150.

9598±

0.00

220.

5706±

0.00

353.

210±

0.01

420

.760±

0.08

5-4

.23±

0.49

-14.

6±8.

4H

AR

PS-N

S576

50.5

464

-3.0

408±

1.22

973.

9801±

0.01

250.

9684±

0.00

210.

5722±

0.00

343.

260±

0.01

321

.330±

0.08

6-5

.54±

0.55

13.8±

6.5

HA

RPS

-NS5

7651

.593

6-4

.034

1±1.

2429

3.74

89±

0.01

360.

9549±

0.00

220.

5702±

0.00

343.

225±

0.01

420

.925±

0.08

1-3

.02±

0.60

11.6±

6.7

HA

RPS

-NS5

7652

.609

71.

1585±

1.19

793.

7852±

0.01

180.

9569±

0.00

220.

5697±

0.00

343.

199±

0.01

420

.569±

0.08

7-6

.23±

0.51

0.1±

9.4

HA

RPS

-NS5

7653

.616

73.

0905±

1.25

063.

7946±

0.01

220.

9635±

0.00

220.

5701±

0.00

353.

273±

0.01

321

.458±

0.08

0-5

.48±

0.56

4.7±

6.6

HA

RPS

-NS5

7679

.500

4-9

.980

9±1.

2040

4.25

30±

0.01

160.

9842±

0.00

210.

5764±

0.00

343.

236±

0.01

420

.845±

0.08

5-3

.62±

0.49

-8.3±

7.3

HA

RPS

-NS5

7680

.546

1-7

.451

0±1.

3384

4.14

85±

0.01

160.

9768±

0.00

210.

5734±

0.00

343.

302±

0.01

321

.774±

0.08

7-2

.91±

0.53

17.4±

7.9

HA

RPS

-NS5

7681

.585

2-7

.364

9±1.

2513

4.21

32±

0.00

950.

9918±

0.00

200.

5740±

0.00

343.

307±

0.01

321

.738±

0.08

3-3

.32±

0.44

4.4±

9.0

HA

RPS

-NS5

7683

.534

45.

1337±

1.28

003.

9168±

0.01

250.

9552±

0.00

220.

5714±

0.00

343.

271±

0.01

321

.334±

0.08

0-7

.17±

0.58

13.1±

7.4

HA

RPS

-NS5

7702

.445

0-2

.463

8±1.

4217

4.04

00±

0.01

990.

9798±

0.00

210.

5716±

0.00

343.

261±

0.01

321

.246±

0.08

3-7

.27±

0.94

-2.7±

9.8

HA

RPS

-NS5

7703

.620

5-4

.753

7±1.

3979

4.06

22±

0.01

620.

9679±

0.00

210.

5709±

0.00

343.

253±

0.01

321

.131±

0.08

2-3

.33±

0.68

7.5±

8.1

HA

RPS

-NS5

7721

.556

62.

2866±

1.59

254.

3654±

0.02

430.

9876±

0.00

210.

5808±

0.00

333.

190±

0.01

420

.571±

0.09

0-8

.56±

1.12

-11.

9±10

.8H

AR

PS-N

S577

27.4

281

5.92

23±

1.23

664.

2273±

0.01

160.

9834±

0.00

210.

5759±

0.00

343.

276±

0.01

321

.325±

0.08

0-6

.57±

0.50

0.3±

7.0

HA

RPS

-NS5

7728

.522

36.

0193±

1.70

224.

0941±

0.01

950.

9784±

0.00

210.

5750±

0.00

343.

244±

0.01

421

.025±

0.07

5-6

.09±

0.84

3.9±

10.8

HA

RPS

-NS5

7729

.416

14.

5281±

1.28

254.

2733±

0.01

570.

9985±

0.00

200.

5745±

0.00

343.

245±

0.01

420

.994±

0.08

0-5

.31±

0.65

5.1±

8.5

HA

RPS

-NS5

7730

.396

811

.728

9±1.

1568

4.13

57±

0.01

000.

9674±

0.00

210.

5714±

0.00

343.

190±

0.01

420

.399±

0.08

6-8

.54±

0.39

-11.

3±6.

5H

AR

PS-N

S577

34.5

708

-3.1

547±

1.59

984.

0872±

0.01

520.

9882±

0.00

200.

5823±

0.00

333.

230±

0.01

420

.656±

0.08

3-2

.86±

0.64

14.3±

10.2

HA

RPS

-NS5

7735

.448

8-7

.804

2±1.

4418

4.54

82±

0.01

771.

0293±

0.00

190.

5774±

0.00

333.

234±

0.01

420

.867±

0.08

7-5

.66±

0.71

-7.4±

10.9

HA

RPS

-NS5

7736

.446

3-8

.725

3±1.

1742

4.31

67±

0.01

120.

9975±

0.00

200.

5730±

0.00

343.

236±

0.01

420

.857±

0.08

5-4

.61±

0.47

8.3±

6.7

HA

RPS

-NS5

7738

.433

40.

5254±

1.33

994.

2030±

0.01

380.

9895±

0.00

200.

5720±

0.00

343.

251±

0.01

421

.098±

0.08

1-4

.59±

0.58

7.2±

7.4

HA

RPS

-NS5

7749

.458

110

.701

6±1.

3292

4.01

14±

0.01

510.

9830±

0.00

210.

5748±

0.00

343.

270±

0.01

321

.288±

0.08

4-4

.18±

0.66

1.5±

8.8

HA

RPS

-NS5

7750

.476

68.

2896±

1.59

704.

0536±

0.02

240.

9862±

0.00

210.

5738±

0.00

343.

251±

0.01

421

.179±

0.08

7-9

.61±

1.06

-3.4±

10.8

HA

RPS

-NS5

7753

.478

94.

3611±

2.22

104.

3139±

0.03

310.

9898±

0.00

200.

5880±

0.00

323.

208±

0.01

420

.805±

0.09

3-2

.18±

1.88

1.0±

14.0

HA

RPS

-NS5

7762

.421

8-3

.159

6±1.

2899

4.41

77±

0.01

611.

0063±

0.00

200.

5756±

0.00

343.

247±

0.01

421

.043±

0.08

0-2

.76±

0.66

-2.1±

7.8

HA

RPS

-NS5

7984

.632

31.

0962±

1.24

963.

8280±

0.01

050.

9394±

0.00

230.

5710±

0.00

353.

263±

0.01

321

.070±

0.08

4-3

.78±

0.49

-5.1±

6.6

HA

RPS

-NS6

7995

.686

56.

3189±

1.18

394.

2605±

0.01

220.

9831±

0.00

210.

5738±

0.00

343.

197±

0.01

420

.406±

0.08

0-4

.55±

0.49

-3.3±

5.7

HA

RPS

-NS6

8019

.732

5-2

.538

0±1.

3792

3.97

44±

0.01

640.

9538±

0.00

220.

5744±

0.00

343.

249±

0.01

421

.004±

0.07

7-4

.68±

0.72

-3.9±

8.7

HA

RPS

-NS6

8022

.684

3-7

.711

7±1.

2749

3.83

66±

0.01

240.

9528±

0.00

220.

5738±

0.00

343.

227±

0.01

420

.741±

0.08

2-5

.24±

0.55

1.7±

6.7

HA

RPS

-NS6

8024

.645

5-1

1.45

91±

1.45

153.

6429±

0.01

140.

9241±

0.00

230.

5695±

0.00

353.

223±

0.01

420

.722±

0.08

3-1

.98±

0.52

-1.6±

8.8

HA

RPS

-NS6

8025

.631

7-1

1.46

48±

1.44

883.

6327±

0.01

100.

9122±

0.00

240.

5670±

0.00

353.

189±

0.01

420

.408±

0.08

3-4

.17±

0.49

1.3±

9.8

HA

RPS

-NS6

8031

.556

013

.055

4±1.

2623

3.77

35±

0.01

150.

9377±

0.00

230.

5697±

0.00

353.

249±

0.01

421

.006±

0.08

3-7

.90±

0.53

-0.6±

8.1

HA

RPS

-NS6

8031

.621

611

.960

1±1.

4224

3.73

46±

0.01

400.

9387±

0.00

230.

5704±

0.00

343.

255±

0.01

321

.122±

0.08

1-8

.02±

0.66

-0.1±

10.9

HA

RPS

-NS6

8037

.690

1-4

.846

4±1.

0332

4.18

20±

0.01

750.

9778±

0.00

210.

5796±

0.00

333.

255±

0.01

321

.120±

0.08

0-3

.62±

0.78

16.0±

8.9

HA

RPS

-NS6


A&A proofs: manuscript no. Gl49b_finalTa

ble

A.1

.Rad

ialv

eloc

ityan

dac

tivity

indi

cato

rtim

e-se

ries

data

ofG

l49

from

HIR

ES,

HA

RPS

-N,a

ndC

AR

ME

NE

Sin

stru

men

ts(c

ont.)

.

BJD

RVC

aHK

/CaI

RT

Hα

Nai

FWH

MC

ON

BIS

CR

Xin

stru

men

t[d

]-24

5000

0[m

s−1 ]

[km

s−1 ]

[%]

[ms−

1 ][m

s−1

N−

1p

]an

dse

ason

7395

.313

22.

1218±

1.40

921.

7454±

0.00

250.

9357±

0.00

150.

6425±

0.00

384.

875±

0.02

220

.633±

0.06

5-2

.52±

0.92

-22.

7±12

.7C

AR

ME

NE

SS4

7397

.334

7-4

.740

7±1.

7413

1.79

98±

0.00

300.

9741±

0.00

180.

6701±

0.00

494.

905±

0.01

920

.608±

0.05

76.

95±

1.08

8.6±

11.9

CA

RM

EN

ES

S473

98.3

453

-9.0

309±

2.39

981.

7047±

0.00

340.

9045±

0.00

21..

.4.

900±

0.02

220

.673±

0.06

54.

64±

1.31

21.6±

20.5

CA

RM

EN

ES

S474

01.2

735

-1.3

684±

1.11

321.

6720±

0.00

140.

8741±

0.00

080.

6139±

0.00

214.

893±

0.02

120

.581±

0.06

25.

01±

0.53

14.6±

8.4

CA

RM

EN

ES

S474

19.3

799

-6.1

178±

1.56

741.

6630±

0.00

220.

8734±

0.00

110.

5899±

0.00

314.

884±

0.02

120

.761±

0.06

35.

04±

0.69

15.0±

15.3

CA

RM

EN

ES

S474

26.3

531

4.25

65±

1.29

431.

6725±

0.00

180.

8821±

0.00

100.

6134±

0.00

314.

871±

0.02

120

.781±

0.06

4-2

.83±

0.63

27.9±

10.8

CA

RM

EN

ES

S475

92.6

888

-1.9

402±

2.08

411.

7091±

0.00

170.

9154±

0.00

100.

6451±

0.00

244.

898±

0.02

120

.263±

0.06

12.

59±

0.63

17.4±

11.9

CA

RM

EN

ES

S576

04.6

843

-7.2

872±

2.43

851.

6454±

0.00

230.

8784±

0.00

130.

5340±

0.00

304.

888±

0.02

220

.763±

0.06

41.

05±

0.82

30.6±

13.3

CA

RM

EN

ES

S576

07.6

875

3.78

22±

2.22

501.

6488±

0.00

220.

8797±

0.00

120.

5920±

0.00

294.

889±

0.02

220

.628±

0.06

4-4

.88±

0.76

-0.4±

16.6

CA

RM

EN

ES

S576

11.6

100

1.62

09±

2.65

041.

6228±

0.00

410.

8715±

0.00

250.

5876±

0.00

774.

862±

0.02

320

.640±

0.06

88.

58±

1.59

75.8±

21.8

CA

RM

EN

ES

S576

29.4

806

4.14

86±

2.37

231.

6599±

0.00

400.

8903±

0.00

250.

5834±

0.00

744.

868±

0.02

420

.676±

0.06

90.

05±

1.54

-2.9±

22.2

CA

RM

EN

ES

S576

33.5

910

-3.4

593±

2.26

151.

6559±

0.00

410.

8866±

0.00

240.

5877±

0.00

664.

899±

0.01

820

.671±

0.05

20.

26±

1.52

3.3±

22.2

CA

RM

EN

ES

S576

35.5

553

6.21

93±

2.19

561.

6756±

0.00

410.

9065±

0.00

240.

6010±

0.00

674.

868±

0.02

120

.643±

0.06

16.

60±

1.52

-36.

4±21

.1C

AR

ME

NE

SS5

7644

.600

5-1

.604

4±1.

4913

1.68

24±

0.00

230.

9030±

0.00

140.

5316±

0.00

334.

874±

0.02

020

.789±

0.05

9-6

.56±

0.87

2.5±

13.7

CA

RM

EN

ES

S576

45.6

629

2.25

04±

1.81

861.

6488±

0.00

270.

8785±

0.00

160.

5879±

0.00

434.

869±

0.02

120

.703±

0.06

3-2

.27±

1.01

11.4±

14.7

CA

RM

EN

ES

S576

47.4

826

-2.6

226±

2.22

161.

6700±

0.00

360.

8997±

0.00

220.

5884±

0.00

624.

867±

0.02

220

.797±

0.06

41.

87±

1.38

2.5±

21.6

CA

RM

EN

ES

S576

49.6

315

-1.7

279±

2.51

591.

6661±

0.00

420.

8962±

0.00

240.

5306±

0.00

654.

898±

0.01

920

.961±

0.05

51.

16±

1.48

-26.

8±22

.9C

AR

ME

NE

SS5

7653

.534

63.

4239±

1.92

641.

6683±

0.00

400.

9105±

0.00

230.

5994±

0.00

614.

855±

0.01

820

.722±

0.05

4-1

.13±

1.45

-8.3±

18.0

CA

RM

EN

ES

S576

57.4

684

-2.7

971±

1.61

431.

6691±

0.00

280.

8905±

0.00

170.

5907±

0.00

444.

893±

0.02

120

.771±

0.06

3-7

.78±

1.05

-4.2±

14.4

CA

RM

EN

ES

S576

78.3

952

-6.7

376±

1.59

531.

7491±

0.00

260.

9395±

0.00

160.

6308±

0.00

404.

891±

0.01

920

.556±

0.05

68.

16±

0.96

0.6±

15.3

CA

RM

EN

ES

S576

85.3

671

0.75

47±

2.68

241.

7304±

0.00

460.

9193±

0.00

290.

6345±

0.00

884.

854±

0.02

320

.553±

0.06

6-7

.22±

1.77

-24.

0±25

.3C

AR

ME

NE

SS5

7688

.449

9-6

.500

7±1.

8055

1.63

20±

0.00

290.

8650±

0.00

170.

5835±

0.00

454.

900±

0.01

920

.658±

0.05

63.

99±

1.10

-4.5±

16.5

CA

RM

EN

ES

S576

98.4

636

0.54

80±

2.59

621.

7500±

0.00

470.

9549±

0.00

290.

6692±

0.00

884.

876±

0.01

820

.787±

0.05

49.

11±

1.78

12.1±

25.9

CA

RM

EN

ES

S577

03.4

153

-1.2

194±

1.52

901.

6781±

0.00

270.

8865±

0.00

160.

5484±

0.00

404.

876±

0.02

220

.774±

0.06

57.

30±

1.00

27.6±

13.2

CA

RM

EN

ES

S577

04.4

039

-2.2

163±

1.65

871.

6849±

0.00

280.

8890±

0.00

160.

5513±

0.00

414.

891±

0.02

020

.813±

0.05

92.

15±

1.03

31.1±

15.5

CA

RM

EN

ES

S577

05.3

366

-0.9

255±

2.25

601.

7074±

0.00

450.

8984±

0.00

270.

6381±

0.00

834.

850±

0.02

020

.464±

0.05

98.

94±

1.74

34.4±

21.6

CA

RM

EN

ES

S577

11.3

008

14.3

086±

2.56

421.

6976±

0.00

510.

9071±

0.00

300.

6619±

0.00

974.

867±

0.02

220

.657±

0.06

4-2

.66±

1.90

1.3±

25.5

CA

RM

EN

ES

S577

38.4

123

2.61

32±

2.63

221.

7044±

0.00

420.

9096±

0.00

260.

6233±

0.00

804.

853±

0.02

220

.524±

0.06

514

.10±

1.66

-45.

6±19

.8C

AR

ME

NE

SS5

7766

.287

82.

5401±

1.61

331.

6645±

0.00

260.

8855±

0.00

150.

5390±

0.00

364.

906±

0.02

020

.891±

0.05

9-3

.45±

0.93

13.9±

14.0

CA

RM

EN

ES

S577

69.3

293

11.6

179±

3.73

181.

6781±

0.00

620.

8983±

0.00

380.

6474±

0.01

324.

865±

0.01

820

.713±

0.05

419

.80±

2.47

-64.

2±26

.9C

AR

ME

NE

SS5

7896

.667

01.

2891±

1.54

081.

6949±

0.00

160.

8967±

0.00

100.

6712±

0.00

264.

871±

0.02

120

.439±

0.06

0-7

.29±

0.61

10.4±

10.8

CA

RM

EN

ES

S679

38.6

472

1.96

46±

2.04

591.

7010±

0.00

180.

8936±

0.00

110.

6142±

0.00

274.

929±

0.01

720

.622±

0.05

1-1

.81±

0.70

-11.

5±10

.9C

AR

ME

NE

SS6

7944

.670

40.

3636±

1.58

411.

7074±

0.00

180.

9068±

0.00

110.

6176±

0.00

264.

912±

0.02

120

.512±

0.06

28.

72±

0.68

-17.

9±11

.7C

AR

ME

NE

SS6

7945

.615

3-4

.171

9±1.

2965

1.71

37±

0.00

170.

9142±

0.00

110.

6294±

0.00

254.

919±

0.01

920

.495±

0.05

40.

86±

0.65

-4.1±

8.9

CA

RM

EN

ES

S679

46.6

684

-6.0

562±

1.80

751.

7194±

0.00

260.

9115±

0.00

160.

6390±

0.00

394.

891±

0.02

120

.465±

0.06

112

.53±

0.97

-14.

6±15

.8C

AR

ME

NE

SS6

7949

.630

9-8

.918

3±1.

4644

1.62

00±

0.00

180.

8410±

0.00

110.

5794±

0.00

254.

880±

0.02

020

.590±

0.05

9-0

.02±

0.68

21.5±

10.2

CA

RM

EN

ES

S679

50.5

906

-7.3

860±

1.44

161.

6190±

0.00

170.

8427±

0.00

100.

5774±

0.00

254.

885±

0.02

020

.595±

0.06

0-0

.83±

0.66

-9.2±

10.9

CA

RM

EN

ES

S679

54.6

366

9.35

90±

1.66

501.

6387±

0.00

170.

8747±

0.00

100.

5927±

0.00

254.

920±

0.01

820

.639±

0.05

3-2

.54±

0.65

9.4±

12.3

CA

RM

EN

ES

S679

60.5

181

4.07

81±

1.75

881.

7452±

0.00

190.

9416±

0.00

120.

6298±

0.00

294.

903±

0.02

220

.642±

0.06

59.

14±

0.71

37.8±

15.0

CA

RM

EN

ES

S679

62.6

565

4.05

16±

1.52

721.

7730±

0.00

180.

9631±

0.00

110.

6543±

0.00

274.

901±

0.02

120

.520±

0.06

15.

10±

0.67

12.8±

13.6

CA

RM

EN

ES

S679

63.6

746

4.41

56±

1.63

141.

6999±

0.00

170.

9042±

0.00

100.

6178±

0.00

244.

922±

0.02

220

.526±

0.06

35.

67±

0.64

16.5±

13.8

CA

RM

EN

ES

S679

64.6

424

0.17

82±

1.59

241.

7259±

0.00

180.

9166±

0.00

110.

6377±

0.00

274.

897±

0.02

220

.495±

0.06

55.

28±

0.68

-15.

9±13

.9C

AR

ME

NE

SS6

7965

.667

3-2

.600

9±1.

8440

1.68

53±

0.00

180.

8901±

0.00

110.

6166±

0.00

264.

883±

0.02

220

.514±

0.06

59.

31±

0.68

30.8±

15.0

CA

RM

EN

ES

S679

68.6

252

-2.3

128±

1.64

051.

6294±

0.00

170.

8526±

0.00

100.

5803±

0.00

244.

903±

0.01

920

.671±

0.05

7-0

.29±

0.64

31.3±

14.0

CA

RM

EN

ES

S679

90.5

161

5.92

62±

1.73

341.

6427±

0.00

170.

8702±

0.00

100.

5911±

0.00

254.

900±

0.02

220

.559±

0.06

40.

91±

0.65

11.1±

14.9

CA

RM

EN

ES

S679

97.5

845

-1.4

014±

1.36

621.

6962±

0.00

170.

9030±

0.00

100.

6098±

0.00

244.

906±

0.02

020

.599±

0.06

0-1

.86±

0.64

2.7±

9.9

CA

RM

EN

ES

S680

00.5

295

5.91

64±

1.27

371.

6825±

0.00

180.

8946±

0.00

110.

6061±

0.00

254.

906±

0.02

020

.631±

0.06

0-2

.32±

0.66

-31.

1±9.

0C

AR

ME

NE

SS6

8000

.663

14.

8073±

1.36

621.

6715±

0.00

210.

8842±

0.00

130.

6014±

0.00

334.

908±

0.02

120

.537±

0.06

1-0

.51±

0.82

-28.

9±10

.4C

AR

ME

NE

SS6

8005

.514

7-7

.876

6±1.

4763

1.64

10±

0.00

180.

8635±

0.00

110.

5966±

0.00

284.

893±

0.02

120

.602±

0.06

00.

86±

0.73

-0.2±

13.0

CA

RM

EN

ES

S680

06.5

883

-6.5

700±

1.79

501.

6366±

0.00

300.

8577±

0.00

180.

5852±

0.00

494.

852±

0.01

920

.560±

0.05

7-7

.97±

1.16

-13.

3±15

.5C

AR

ME

NE

SS6

8007

.523

6-6

.294

7±1.

5762

1.62

04±

0.00

220.

8483±

0.00

130.

5774±

0.00

314.

900±

0.01

920

.746±

0.05

5-8

.01±

0.83

3.5±

14.0

CA

RM

EN

ES

S680

08.5

384

-2.3

464±

1.43

511.

6346±

0.00

170.

8649±

0.00

100.

5910±

0.00

244.

889±

0.02

020

.518±

0.05

7-1

0.92±

0.65

-12.

5±10

.1C

AR

ME

NE

SS6

8009

.482

91.

2484±

1.42

471.

6204±

0.00

180.

8453±

0.00

110.

5821±

0.00

264.

893±

0.02

020

.558±

0.05

7-4

.95±

0.69

-28.

6±11

.4C

AR

ME

NE

SS6



ble

A.1

.Rad

ialv

eloc

ityan

dac

tivity

indi

cato

rtim

e-se

ries

data

ofG

l49

from

HIR

ES,

HA

RPS

-N,a

ndC

AR

ME

NE

Sin

stru

men

ts(c

ont.)

.

BJD

RVC

aHK

/CaI

RT

Hα

Nai

FWH

MC

ON

BIS

CR

Xin

stru

men

t[d

]-24

5000

0[m

s−1 ]

[km

s−1 ]

[%]

[ms−

1 ][m

s−1

N−

1p

]an

dse

ason

8023

.309

9-1

0.09

60±

1.84

041.

6410±

0.00

210.

8680±

0.00

120.

5854±

0.00

324.

877±

0.02

220

.694±

0.06

4-0

.79±

0.77

-1.4±

16.1

CA

RM

EN

ES

S680

27.5

570

-0.4

132±

1.31

671.

6033±

0.00

160.

8400±

0.00

090.

5700±

0.00

214.

900±

0.01

920

.674±

0.05

6-1

2.06±

0.59

0.6±

10.5

CA

RM

EN

ES

S680

28.5

945

5.17

40±

1.74

961.

6025±

0.00

160.

8439±

0.00

100.

5651±

0.00

224.

903±

0.02

020

.722±

0.05

9-1

5.13±

0.60

-21.

7±11

.9C

AR

ME

NE

SS6

8030

.434

614

.502

4±1.

8300

1.68

86±

0.00

170.

9029±

0.00

100.

6255±

0.00

244.

907±

0.01

920

.682±

0.05

6-1

3.35±

0.63

0.4±

13.0

CA

RM

EN

ES

S680

40.5

104

-1.0

588±

2.18

421.

6694±

0.00

170.

8819±

0.00

100.

5854±

0.00

224.

890±

0.02

020

.494±

0.05

7-4

.25±

0.61

28.0±

11.8

CA

RM

EN

ES

S680

50.4

911

5.61

64±

1.82

661.

6460±

0.00

170.

8664±

0.00

100.

5960±

0.00

244.

907±

0.01

920

.621±

0.05

7-1

.74±

0.63

5.8±

13.0

CA

RM

EN

ES

S680

54.4

417

0.19

65±

1.59

331.

6647±

0.00

190.

8786±

0.00

110.

6044±

0.00

254.

884±

0.02

220

.582±

0.06

3-0

.76±

0.67

5.7±

11.3

CA

RM

EN

ES

S680

60.3

782

-0.8

298±

1.40

191.

6498±

0.00

220.

8630±

0.00

130.

5992±

0.00

314.

881±

0.02

320

.515±

0.06

66.

36±

0.80

-10.

9±11

.9C

AR

ME

NE

SS6

8066

.422

71.

6766±

1.47

391.

5857±

0.00

220.

8225±

0.00

140.

5870±

0.00

354.

886±

0.02

120

.731±

0.06

14.

68±

0.87

-13.

2±11

.4C

AR

ME

NE

SS6

8081

.370

6-5

.903

2±1.

8106

1.61

16±

0.00

250.

8407±

0.00

140.

5831±

0.00

354.

869±

0.01

920

.705±

0.05

51.

08±

0.94

13.5±

15.0

CA

RM

EN

ES

S680

84.4

080

8.40

98±

1.77

981.

5845±

0.00

180.

8243±

0.00

100.

5714±

0.00

24..

...

...

.-2

3.5±

11.0

CA

RM

EN

ES

S680

92.5

559

-5.8

962±

1.34

551.

6467±

0.00

150.

8788±

0.00

080.

6046±

0.00

204.

891±

0.02

120

.744±

0.06

35.

32±

0.52

5.2±

8.6

CA

RM

EN

ES

S680

95.3

943

-0.7

760±

1.31

381.

6389±

0.00

160.

8645±

0.00

090.

5479±

0.00

214.

913±

0.01

920

.823±

0.05

81.

65±

0.59

-22.

2±9.

6C

AR

ME

NE

SS6

8099

.295

80.

4674±

2.05

421.

6184±

0.00

390.

8497±

0.00

240.

5055±

0.00

724.

874±

0.02

020

.839±

0.05

94.

25±

1.53

16.3±

18.4

CA

RM

EN

ES

S681

14.3

012

3.27

27±

2.42

921.

6598±

0.00

360.

8842±

0.00

220.

5076±

0.00

624.

892±

0.02

020

.947±

0.05

9-2

.19±

1.37

1.0±

19.2

CA

RM

EN

ES

S681

21.4

035

1.69

09±

1.33

771.

5951±

0.00

160.

8329±

0.00

090.

5265±

0.00

214.

892±

0.02

020

.755±

0.05

9-5

.02±

0.59

-4.7±

9.7

CA

RM

EN

ES

S681

23.3

299

1.39

96±

1.44

611.

6447±

0.00

180.

8572±

0.00

100.

5508±

0.00

244.

899±

0.01

920

.740±

0.05

60.

54±

0.65

24.0±

10.1

CA

RM

EN

ES

S681

41.4

369

5.05

53±

1.25

301.

6591±

0.00

160.

8683±

0.00

090.

5579±

0.00

224.

881±

0.02

020

.821±

0.05

93.

65±

0.56

-10.

5±8.

9C

AR

ME

NE

SS6

8143

.431

42.

6230±

1.45

871.

6530±

0.00

160.

8606±

0.00

090.

5476±

0.00

244.

897±

0.02

020

.791±

0.06

03.

51±

0.57

3.7±

9.2

CA

RM

EN

ES

S681

49.3

838

-1.4

678±

1.41

721.

6744±

0.00

200.

8939±

0.00

120.

5463±

0.00

284.

881±

0.02

220

.719±

0.06

40.

77±

0.71

13.7±

11.4

CA

RM

EN

ES

S681

61.3

404

1.72

13±

1.84

071.

6459±

0.00

210.

8631±

0.00

130.

5284±

0.00

324.

898±

0.01

920

.761±

0.05

7-0

.87±

0.79

-4.0±

11.9

CA

RM

EN

ES

S681

66.3

726

3.98

16±

1.71

221.

6575±

0.00

240.

8741±

0.00

140.

5293±

0.00

404.

884±

0.02

220

.825±

0.06

55.

21±

0.90

11.6±

14.7

CA

RM

EN

ES

S681

67.3

469

4.69

90±

2.59

451.

6835±

0.00

450.

8940±

0.00

270.

5320±

0.00

884.

819±

0.02

320

.702±

0.06

813

.88±

1.68

-11.

1±21

.9C

AR

ME

NE

SS6

8172

.286

14.

4350±

1.95

991.

6413±

0.00

340.

8740±

0.00

210.

5157±

0.00

584.

866±

0.02

620

.723±

0.07

7-4

.98±

1.29

5.0±

14.6

CA

RM

EN

ES

S681

73.2

864

-4.3

313±

1.78

451.

6852±

0.00

180.

8987±

0.00

110.

6105±

0.00

264.

884±

0.02

020

.737±

0.05

83.

85±

0.67

5.4±

12.4

CA

RM

EN

ES

S681

74.2

934

-5.3

571±

2.03

001.

6451±

0.00

250.

8703±

0.00

150.

6049±

0.00

414.

898±

0.01

920

.789±

0.05

56.

17±

0.96

-3.6±

11.3

CA

RM

EN

ES

S681

75.3

066

-5.0

708±

1.31

831.

6411±

0.00

200.

8697±

0.00

110.

5949±

0.00

284.

878±

0.01

920

.817±

0.05

65.

11±

0.73

-8.6±

9.1

CA

RM

EN

ES

S6



50

0

50

a) EXORAP B

0.0

0.2

250

2550

b) EXORAP V

0.0

0.1

25

0

25

c) EXORAP R

0.0

0.1

0.2

7500 7600 7700 7800 7900 8000 8100

25

0

25

d) EXORAP I

0.0

0.2

0.4

0

50

c) SNO V

0.00

0.25

0.50

8260 8280 8300 8320 8340 8360 8380 8400

50

0

50

d) SNO R

0.00

0.25

0.50

4000 5000 6000 7000 8000

100

0

100

e) ASAS V

0.00

0.02

0.04

5000 5500 6000 6500 7000 7500BJD [d] - 2450000

25

0

25

g) MEarth V

0.00 0.05 0.10 0.15 0.20 0.25Frequency [day 1]

0.0

0.1

0.2

Fig. A.1. Differential photometric data of Gl 49 in mmag (left panels; we note the inverted y axis) and GLS periodograms (right panels) includingEXORAP, SNO, ASAS, and MEarth. The vertical orange lines indicate periods of 9.37 and 18.86 d, the red vertical line a period of 13.85 d, andthe horizontal dashed red lines the 0.1, 1, and 10 % analytical FAP.



4

5a) CaHK index

0.0

0.2

0.4

0.9

1.0b) H index

0.0

0.2

0.4

0.56

0.58

0.60 c) NaI index

0.00

0.05

0.10

0.15

3.2

3.3d) FWHM [kms 1]

0.0

0.2

0.4

20

21

22e) CON [%]

0.0

0.2

0.4

10

5

0

5f) BIS [ms 1]

0.00

0.05

0.10

0.15

0 500 1000 1500 2000BJD [d] - 2456000

25

0

25 g) CRX [ms 1N 1p ]

0.000 0.025 0.050 0.075 0.100 0.125 0.150 0.175Frequency [day 1]

0.0

0.1

0.2

Fig. A.2. Seven activity indices calculated from HARPS-N spectra. On the left we show the time series of (from top to bottom) the CaHK, Hα,NaI, FWHM, CON, BIS, and CRX indices. On the right we show the periodograms of those datasets excluding HARPS-N S1 (marked in greenon the left). For more details see Fig A.1.



1.6

1.7

1.8a) CaIRT index

0.0

0.2

0.4

0.85

0.90

0.95 b) H index

0.0

0.2

0.4

0.50

0.55

0.60

0.65 c) NaI index

0.0

0.1

0.2

0.3

4.80

4.85

4.90

4.95d) FWHM [kms 1]

0.0

0.1

0.2

20.25

20.50

20.75

21.00 e) CON [%]

0.0

0.1

0.2

0.3

0

20 f) BIS [ms 1]

0.0

0.1

0.2

0.3

1400 1600 1800 2000 2200BJD [d] - 2456000

100

0

100g) CRX [ms 1N 1

p ]

0.000 0.025 0.050 0.075 0.100 0.125 0.150 0.175Frequency [day 1]

0.0

0.1

0.2

Fig. A.3. Same as Fig. A.2 but for the CARMENES data.



Fig. A.4. Corner plot of the fitted parameters of the MCMC solutions using a Keplerian orbit (parameters P, K, e sinω, e cosω, Tper) and a GPnoise term (hyper-parameters PQP, KQP, w, λ) to the RVs of HARPS-N S2 to S6 and CARMENES of Gl 49. The solutions included in this plotexceed the ln L value of a fit with a GP term only (see Table 4) plus the 1 % FAP value that we calculate empirically in Sect. 4.2.


Date post:	27-Oct-2019
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

A HADES and CARMENES collaboration - arxiv.org · A&A proofs: manuscript no. Gl49b_ﬁnal...

Documents