arXiv:1402.2655v3 [astro-ph.CO] 21 Dec 2015 Draft version September 4, 2018 Preprint typeset using L A T E X style emulateapj v. 5/2/11 AN HST/COS SURVEY OF THE LOW-REDSHIFT INTERGALACTIC MEDIUM. I. SURVEY, METHODOLOGY, AND OVERALL RESULTS Charles W. Danforth, Brian A. Keeney, Evan M. Tilton, J. Michael Shull, John T. Stocke, Matthew Stevans 1 , Matthew M. Pieri 2 , Blair D. Savage 3 , Kevin France, David Syphers 4 , Britton D. Smith 5 , James C. Green, Cynthia Froning 1 , Steven V. Penton 6 , & Steven N. Osterman 7 CASA, Department of Astrophysical & Planetary Sciences, University of Colorado, 389-UCB, Boulder, CO, USA 80309; [email protected]Draft version September 4, 2018 ABSTRACT We use high-quality, medium-resolution Hubble Space Telescope/Cosmic Origins Spectrograph (HST/COS) observations of 82 UV-bright AGN at redshifts z AGN < 0.85 to construct the largest survey of the low-redshift intergalactic medium (IGM) to date: 5138 individual extragalactic ab- sorption lines in H I and 25 different metal-ion species grouped into 2611 distinct redshift sys- tems at z abs < 0.75 covering total redshift pathlengths Δz HI = 21.7 and Δz OVI = 14.5. Our semi-automated line-finding and measurement technique renders the catalog as objectively-defined as possible. The cumulative column-density distribution of H I systems can be parametrized dN (>N )/dz = C 14 (N/10 14 cm −2 ) −(β−1) , with C 14 = 25 ± 1 and β =1.65 ± 0.02. This distri- bution is seen to evolve both in amplitude, C 14 ∝ (1 + z ) 2.3±0.1 , and slope β(z )=1.75 − 0.31 z for z ≤ 0.47. We observe metal lines in 418 systems, and find that the fraction of IGM absorbers detected in metals is strongly dependent on N HI . The distribution of O VI absorbers appear to evolve in the same sense as the Lyα forest. We calculate contributions to Ω b from different components of the low-z IGM and determine the Lyα decrement as a function of redshift. IGM absorbers are analyzed via a two-point correlation function in velocity space. We find substantial clustering of H I absorbers on scales of Δv = 50 − 300 km s −1 with no significant clustering at Δv 1000 km s −1 . Splitting the sam- ple into strong and weak absorbers, we see that most of the clustering occurs in strong, N HI 10 13.5 cm −2 , metal-bearing IGM systems. The full catalog of absorption lines and fully-reduced spectra is available via the Mikulski Archive for Space Telescopes (MAST) as a high-level science product at http://archive.stsci.edu/prepds/igm/ . Subject headings: astronomical databases: surveys—cosmological parameters—cosmology: observations—intergalactic medium—quasars: absorption lines 1. INTRODUCTION The low-redshift intergalactic medium (IGM) holds many important clues to complete our understanding of cosmology. Even after nearly 14 Gyr of evolution, only a small fraction of baryonic matter has collapsed into lu- minous objects (galaxies, groups, clusters) while ∼ 80% or more still exists as a diffuse, often unvirialized IGM, the distribution and characteristics of which are only be- ginning to be measured (e.g., Shull, Smith, & Danforth 2012). Furthermore, there is a complicated and poorly understood interplay between the IGM, circumgalactic medium (CGM), and stars and gas in galaxies. These gaseous reservoirs provide raw material which is sub- sequently formed into stars and galaxies. These, in turn, enrich the IGM via outflows driven by super- 1 Department of Astronomy, University of Texas at Austin, Austin, TX 78712 2 A*MIDEX, Aix Marseille Universit´ e, CNRS, LAM, UMR7326, Marseille, FR 3 Department of Astronomy, University of Wisconsin, Madi- son, WI, USA, 53706 4 East Washington University, Cheney, WA, USA, 99004 5 Institute for Astronomy, University of Edinburgh, Royal Ob- servatory, Edinburgh EH9 3HJ, UK 6 Space Telescope Science Institute, Baltimore, MD, USA, 21218 7 The Johns Hopkins University Applied Physics Lab, Laurel, MD, USA, 20723 novae, radiation pressure, and active galactic nuclei (AGN Oppenheimer & Dav´ e 2008; Smith et al. 2011). Diffuse intergalactic gas is currently quite difficult to observe in emission (Frank et al. 2012; but see also Steidel et al. 2011; Martin et al. 2014a,b). The most sen- sitive method for detecting most of the gas is through absorption-line spectroscopy using bright background objects (typically AGN) to provide an ultraviolet contin- uum. The highest concentration of strong gas-diagnostic lines is in the rest-frame far-ultraviolet (FUV) band from ∼ 2000 ˚ A shortward to the Lyman edge at 912 ˚ A. Investigating the FUV at low redshift requires opti- mized spectrographs above Earth’s UV-blocking atmo- sphere. Thus, there have been a series of space-based UV spectrographs, both as primary-science instruments on space-borne observatories (Copernicus, International Ultraviolet Explorer, Hopkins Ultraviolet Telescope, Far- Ultraviolet Spectroscopic Explorer) and instruments in- stalled aboard the Hubble Space Telescope or HST: Faint Object Spectrograph (FOS), Goddard High-Resolution Spectrograph (GHRS), Space Telescope Imaging Spec- trograph (STIS), and now the Cosmic Origins Spectro- graph (COS). COS is the fourth-generation UV spectrograph on- board HST and is optimized for medium-resolution (λ/Δλ ≈ 18, 000, Δv ≈ 17 km s −1 ) spectroscopy of point
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Draft version September 4, 2018Preprint typeset using LATEX style emulateapj v. 5/2/11
AN HST/COS SURVEY OF THE LOW-REDSHIFT INTERGALACTIC MEDIUM.I. SURVEY, METHODOLOGY, AND OVERALL RESULTS
Charles W. Danforth, Brian A. Keeney, Evan M. Tilton, J. Michael Shull, John T. Stocke, MatthewStevans1, Matthew M. Pieri2, Blair D. Savage3, Kevin France, David Syphers4, Britton D. Smith5, James C.
Green, Cynthia Froning1, Steven V. Penton6, & Steven N. Osterman7
CASA, Department of Astrophysical & Planetary Sciences, University of Colorado, 389-UCB, Boulder, CO, USA 80309;[email protected]
Draft version September 4, 2018
ABSTRACT
We use high-quality, medium-resolution Hubble Space Telescope/Cosmic Origins Spectrograph(HST/COS) observations of 82 UV-bright AGN at redshifts zAGN < 0.85 to construct the largestsurvey of the low-redshift intergalactic medium (IGM) to date: 5138 individual extragalactic ab-sorption lines in H I and 25 different metal-ion species grouped into 2611 distinct redshift sys-tems at zabs < 0.75 covering total redshift pathlengths ∆zHI = 21.7 and ∆zOVI = 14.5. Oursemi-automated line-finding and measurement technique renders the catalog as objectively-definedas possible. The cumulative column-density distribution of H I systems can be parametrizeddN (> N)/dz = C14(N/1014 cm−2)−(β−1), with C14 = 25 ± 1 and β = 1.65 ± 0.02. This distri-bution is seen to evolve both in amplitude, C14 ∝ (1 + z)2.3±0.1, and slope β(z) = 1.75 − 0.31 z forz ≤ 0.47. We observe metal lines in 418 systems, and find that the fraction of IGM absorbers detectedin metals is strongly dependent on NHI. The distribution of O VI absorbers appear to evolve in thesame sense as the Lyα forest. We calculate contributions to Ωb from different components of the low-zIGM and determine the Lyα decrement as a function of redshift. IGM absorbers are analyzed via atwo-point correlation function in velocity space. We find substantial clustering of H I absorbers onscales of ∆v = 50−300 km s−1 with no significant clustering at ∆v & 1000 km s−1. Splitting the sam-ple into strong and weak absorbers, we see that most of the clustering occurs in strong, NHI & 1013.5
cm−2, metal-bearing IGM systems. The full catalog of absorption lines and fully-reduced spectra isavailable via the Mikulski Archive for Space Telescopes (MAST) as a high-level science product athttp://archive.stsci.edu/prepds/igm/.
The low-redshift intergalactic medium (IGM) holdsmany important clues to complete our understanding ofcosmology. Even after nearly 14 Gyr of evolution, only asmall fraction of baryonic matter has collapsed into lu-minous objects (galaxies, groups, clusters) while ∼ 80%or more still exists as a diffuse, often unvirialized IGM,the distribution and characteristics of which are only be-ginning to be measured (e.g., Shull, Smith, & Danforth2012). Furthermore, there is a complicated and poorlyunderstood interplay between the IGM, circumgalacticmedium (CGM), and stars and gas in galaxies. Thesegaseous reservoirs provide raw material which is sub-sequently formed into stars and galaxies. These, inturn, enrich the IGM via outflows driven by super-
1 Department of Astronomy, University of Texas at Austin,Austin, TX 78712
3 Department of Astronomy, University of Wisconsin, Madi-son, WI, USA, 53706
4 East Washington University, Cheney, WA, USA, 990045 Institute for Astronomy, University of Edinburgh, Royal Ob-
servatory, Edinburgh EH9 3HJ, UK6 Space Telescope Science Institute, Baltimore, MD, USA,
212187 The Johns Hopkins University Applied Physics Lab, Laurel,
MD, USA, 20723
novae, radiation pressure, and active galactic nuclei(AGN Oppenheimer & Dave 2008; Smith et al. 2011).Diffuse intergalactic gas is currently quite difficult to
observe in emission (Frank et al. 2012; but see alsoSteidel et al. 2011; Martin et al. 2014a,b). The most sen-sitive method for detecting most of the gas is throughabsorption-line spectroscopy using bright backgroundobjects (typically AGN) to provide an ultraviolet contin-uum. The highest concentration of strong gas-diagnosticlines is in the rest-frame far-ultraviolet (FUV) band from∼ 2000 A shortward to the Lyman edge at 912 A.Investigating the FUV at low redshift requires opti-mized spectrographs above Earth’s UV-blocking atmo-sphere. Thus, there have been a series of space-basedUV spectrographs, both as primary-science instrumentson space-borne observatories (Copernicus, InternationalUltraviolet Explorer, Hopkins Ultraviolet Telescope, Far-Ultraviolet Spectroscopic Explorer) and instruments in-stalled aboard the Hubble Space Telescope or HST: FaintObject Spectrograph (FOS), Goddard High-ResolutionSpectrograph (GHRS), Space Telescope Imaging Spec-trograph (STIS), and now the Cosmic Origins Spectro-graph (COS).COS is the fourth-generation UV spectrograph on-
board HST and is optimized for medium-resolution(λ/∆λ ≈ 18, 000, ∆v ≈ 17 km s−1) spectroscopy of point
sources in the 1135–1800 A band (Green et al. 2012;Osterman et al. 2011). COS has an effective area that isan order of magnitude larger in the FUV (λ < 1800 A)than previous spectrographs. Furthermore, the excel-lent scattered light control and low background detec-tors of COS mean that fainter objects (Fλ . 10−14
erg cm−2 s−1 A−1) can be observed than with previousinstruments, and higher quality spectra of bright targetscan be obtained in much less time. In its first five yearsof science operations (Green et al. 2012), COS accumu-lated an unprecedented archive of hundreds of AGN spec-tra collected under a broad range of scientific programsfrom AGN physics to studies of the interstellar medium(ISM) in our own Galaxy. Regardless of their originalscientific intent, many of these spectra are suitable forquasar absorption-line studies of the IGM.In this paper, we build on the heritage
of many previous low-z IGM absorber cata-logs from HST/FOS (Bahcall et al. 1993, 1996;Jannuzi et al. 1998; Weymann et al. 1998),HST/GHRS (Penton, Stocke, & Shull 2000), FUSE(Danforth & Shull 2005; Danforth et al. 2006), andHST/STIS (Penton, Stocke, & Shull 2004; Lehner et al.2007; Danforth & Shull 2008; Tripp et al. 2008;Danforth et al. 2010a; Tilton et al. 2012). This currentsurvey represents the largest sample of low-z absorbersto date and is more sensitive than previous studies inmost cases. It was designed to be a general-purposesurvey with applications to a wide variety of extragalac-tic astrophysics: 82 extragalactic sight lines coveringa combined redshift pathlength in H I of ∆z = 21.7,5138 intergalactic absorption lines comprising 2611distinct redshift systems, and detections of 25 differentmetal-ion species. We describe the sample selectioncriteria, data reduction methods, and semi-automatedmeasurement techniques in Section 2. We present thecatalog and substantial electronic resources available tousers of the survey in Section 3. Overall results from thesurvey are given in Section 4. In Section 5, we presentsome of the more detailed findings from the survey,including the evolution of H I and metals in the IGMat z ≤ 0.47 and the radial-velocity clustering propertiesof IGM absorbers (the two-point correlation function).Important survey parameters, fitted quantities, andinitial findings are summarized in Section 6.
2. DATA ANALYSIS
2.1. Sight Line Selection
In the past five years, over 400 extragalactic targetshave been observed with COS. The main objective of thisproject is to develop a comprehensive, statistical catalogof intervening absorbers in the low-redshift universe, inparticular weak H I and metal-line systems. Obtaininghigh signal-to-noise data was our first priority in choosingwhich targets to include, which precludes many of thearchival datasets. With a few exceptions, we include onlyspectra with a typical signal-to-noise of S/N & 15 per (∼17 km s−1) COS resolution element in the Lyα forest (seeKeeney et al. 2012, for full discussion of the acheivableS/N in COS data).Secondly, we require reasonably distant targets to max-
imize the redshift pathlength probed by each sight line.Nearby AGN, particularly Seyfert galaxies, are typically
bright and often have exquisite spectra, but their avail-able IGM pathlength is short and often contaminated byabsorption intrinsic to the AGN. We require a sight lineto have ∆zLyα ≥ 0.05 of unobstructed pathlength for in-clusion in our survey. Conversely, high-z targets samplelong IGM pathlengths, but they often suffer from a highline density that makes line identification difficult. Thetwo moderate-resolution FUV channels of COS (G130Mand G160M) are only sensitive to H I absorption throughLyα at z . 0.47 and through Lyβ and higher-order Ly-man transitions at 0.1 . z . 0.9. Thus we set an upperlimit on source redshift of zAGN < 0.9. Similarly, weconcentrate on AGN observed with both the G130M andG160M gratings for the longest possible wavelength cov-erage. We augment the target list with a set of G130M-only observations of targets at z . 0.2 where the entireLyα forest falls within the G130M grating.The COS FUV archive also contains & 130 extragalac-
tic sight lines observed with the low-resolution (∆v & 100km s−1) G140L grating. While these data are often ofhigher quality than those from IUE or HST/FOS or low-resolution modes of GHRS or STIS, they are sensitiveonly to the strongest intervening absorbers, and we havenot utilized them in this survey. However, these low-resolution and/or low-S/N data are useful for studiesof AGN continua (e.g. Shull, Stevans, & Danforth 2012;Stevans et al. 2014), for surveys of strong absorptionlines, and as flux-qualification observations for futuremedium-resolution observations.AGN with strong absorption lines (BALs, mini-BALs)
were not explicitly excluded from the survey. Thereare a few AGN with strong intrinsic absorption (e.g.,RBS542), but these systems are not on the level of aBAL or mini-BAL. In any case, absorption obviously in-trinsic to the absorber was excluded from analysis (asdiscussed in S2.4.2).To constitute our survey, we selected 82 AGN sight
lines from the archive which met these criteria; 68 wereobserved with both FUV medium-resolution gratings(1135 − 1800 A at ∼ 17 km s−1 resolution) in the red-shift range 0.058 ≤ zAGN ≤ 0.852 and an additional 14AGN at 0.07 ≤ zAGN ≤ 0.2 had coverage in G130Monly (1135 − 1450 A). Astronomical target informationis presented in Table 1. Most of the AGN observed inCycles 18–20 under the Guaranteed Time Observationprograms (GTO; PI-Green) are included, along with nu-merous archival datasets collected under various GuestObserver programs. Observational and programatic de-tails are presented in Table 2.Individual exposures for each target were obtained
from the Mikulski Archive for Space Telescopes (MAST).We start with x1d.fits files (individual exposures re-duced to one-dimensional spectra) processed uniformlyby the pipeline software circa mid-20148. All exposuresof each sight line were combined to maximize the S/N ofeach spectrum. The calibrated, one-dimensional spectrafor each target were coadded into a continuous spectrum,usually over the useful range ∼ 1140 − 1790 A employ-ing a custom IDL procedure developed and extensivelytested at University of Colorado.
aMedian S/N per resolution element in the G130M and G160M chan-
nels.
2.2. Data Reduction and Processing
The default pipeline software that produces thex1d.fits files is capable of combining HST/COS FUVdata taken at multiple FP-POS positions for a singleCENWAVE, but it does not combine data across multi-ple CENWAVE positions nor does it combine data takenwith the G130M and G160M gratings. Our IDL pro-cedure coadd x1d was written to serve this purpose,as were several other routines used by other groups,including the counts coadd code used by the COSHalos and COS Dwarfs teams (e.g., Tumlinson et al.2011; Werk et al. 2014; Bordoloi et al. 2014) and thecoscombine code used by the Wisconsin group (e.g.,Savage et al. 2014; Fox et al. 2015; Wakker et al. 2015).While all of these routines share the common goal of op-timally combining data taken across multiple FP-POSand CENWAVE positions and gratings, they do havesome differences in implementation, including the proce-dure by which individual exposures are cross-correlatedand interpolated onto a common wavelength scale. How-ever, the primary philosophical difference between thesecodes is in the units of their output. Our proce-dure, coadd x1d, preserves the flux units of the inputx1d.fits files, while the other two routines create spec-tra whose fluxes are in units of counts/sec, which simpli-fies the Poissonian error treatment. Since the x1d.fitsfiles contain the flux, net counts, and gross counts as afunction of wavelength for each exposure, our procedureuses the gross counts at a given wavelength to determine
a Poissonian uncertainty that is valid in the low-countregime (e.g., Gehrels 1986). This uncertainty arrayis then multiplied by an empirical sensitivity function,which is given by the ratio of the flux and net countsarrays, to convert the uncertainty values into the unitsof the flux array before proceeding with coaddition.Danforth et al. (2010b) describe the basic algorithm of
coadd x1d, but many refinements have been made overthe intervening years. The revised coaddition algorithmproceeds sequentially as follows:
1. The x1d.fits files are opened, loaded into inputarrays, and examined individually.
2. The data quality (DQ) flags from the x1d.fitsfiles are parsed to remove bad pixels. Pixels withsome DQ values9 are instead de-weighted by ar-tificially increasing their uncertainty so that theycontribute half as much to the coadded spectrumthan they otherwise would.
3. The input data are binned (i.e., Nyquist sampled) ifrequested by the user. At this stage the exposure-level uncertainties are optionally modified to in-corporate the non-Poissonian noise characterizedin Keeney et al. (2012), ensuring that the uncer-tainty values reflect the RMS fluctuations about
9 A particular DQ flag is set by flipping one of 15 bits in anunsigned integer. Of these 15 bits, nine are handled identicallyby coadd x1d and the default calcos pipeline, three are handledmore leniently by coadd x1d (pixels with DQ values of 8, 16, 32 arerejected by calcos but only de-weighted by coadd x1d), and threeare handled more strictly by coadd x1d (DQ values of 4, 1024, 4096are de-weighted by coadd x1d and ignored by calcos).
6 Danforth et al.
the continuum level. While neither of these stepsare default behaviors of coadd x1d, all data for thispaper were processed with these flags.
4. A reference exposure is chosen and all other expo-sures are aligned to it using cross correlation in theregions surrounding strong ISM lines (one cross-correlation feature per detector segment). Thisprocedure provides a first-order wavelength align-ment between exposures, which are in the he-liocentric frame output by the calcos pipeline.However, there may be residual shifts of up to afew resolution elements at different wavelengths inthe coadded data (Tripp et al. 2008; Savage et al.2014; Wakker et al. 2015).
5. The flux level of each exposure is scaled to matchthe flux in the reference exposure, and uncertaintyis scaled by this same factor. The majority (75/82)of the AGN sight lines were observed during onlya single epoch, and the changes in source bright-ness over the observing period tended to be negli-gible (only 8% of exposures required a flux scalingof greater than 10%; however, see Danforth et al.2013, for a dramatic counter-example). Observa-tions at different epochs occasionally have signif-icantly different fluxes; these cases are scaled tomatch the first data epoch. We assume no changesin spectral shape or absorption/emission propertiesbetween epochs or exposures. Absolute velocitycalibration of arbitrary HST/COS data to betterthan ∼ 10 km s−1 is problematic owing to the pre-cise location of the spectrum on the detector andthe complicated wavelength solution.
6. The individual exposures are interpolated onto acommon wavelength scale. It is important to usenearest-neighbor interpolation (i.e., shift and add)to minimize the non-Poissonian noise introduced bythe coaddition process (e.g., correlated noise fromlinear interpolation; Keeney et al. 2012).
7. Combine the individual exposures using oneof several weighting methods (arithmetic mean,exposure-time weighting, inverse-variance weight-ing, or signal-to-noise weighting). We use the de-fault inverse-variance weighting for this analysis.
8. Finally, the quantities of interest (wavelength,coadded flux, and coadded uncertainty at a min-imum) are returned to the user.
Next, continua were fitted to each of the coadded datasets using a semi-automated continuum-fitting techniquedeveloped for fitting optical SDSS spectra (Pieri et al.2014) and adapted for use in higher-resolution FUVdatasets. First, the spectra were split into segments of 5-15 A width (width of the bins was adjusted qualitativelybased on how smooth the AGN continuum was and thedensity of strong absorption features). Continuum pixelswithin each segment were identified as those for which theflux-to-error ratio was less than 1.5σ below the medianflux/error value for all the pixels in the segment. Thus,absorption lines (flux lower than the segment median)were excluded, as were regions of unusually strong noise
Fig. 1.— Median-combined “Galactic foreground” coaddition of82 normalized AGN spectra in our survey reveals the Galactic fore-ground absorption present in most extragalactic spectra. A line-dense 100 A spectral region near the blue end of the COS/FUVspectral range is shown. Dotted curves show ±1σ normalizedflux values in the sample at each wavelength. Identified Galac-tic absorption lines are labeled. The full spectrum is available athttp://archive.stsci.edu/prepds/igm/.
(error higher than segment average). The process wasiterated until minimal change occured in the populationof continuum pixels between one iteration and the next,or until only 10% of the original pixels in the segmentremained classified as continuum. The median value ofthe continuum pixels was then recorded as a continuumflux node for the 5-15 A segment and a spline functionwas fitted between the nodes.The continuum fit of each spectrum was then checked
manually and adjusted as needed. The continuum iden-tification and spline-fitting processes worked well in re-gions where continua varied smoothly, but it was oftenpoor in regions of sudden change. For instance, splinefits perform poorly at the sharp peaks of emission lines(cusps), in the Galactic Lyα absorption trough (1210–1220 A), at the absorption edge of partial Lyman limitsystems (Stevans et al. 2014), and at the edges of the de-tectors (λ . 1140 A, λ & 1790 A). The spline fits in theseregions were replaced by piecewise-continuous, low-orderLegendre polynomial fits.
2.3. Absorber Identification and Measurement
A weakness in previous quasar absorption-line surveys(e.g., Penton, Stocke, & Shull 2000; Danforth & Shull2008, hearafter DS08) was the subjective nature of iden-tifying absorption features in normalized data. Wepartially corrected for this bias here by implementingan automated line-finding and measurement algorithmmodeled on that used in the FOS Key Project work(Schneider et al. 1993). We believe this yields a moreobjectively-defined catalog of absorption lines.First, we generated a “line-less error vector” σ(λ) by
interpolating the error σ(λ) in regions defined as contin-uum over those identified as non-continuum during thecontinuum fitting process. We calculated an equivalentwidth vector W (λ) by convolving the normalized fluxpixels with a representative line profile. The line profile isthe COS line spread function (Kriss 2011) convolved witha Gaussian with Doppler parameter, b = 20 km s−1, typ-
ical of narrow absorption lines in the ISM and IGM. Theerror vector σ(λ) was convolved with the same line spreadfunction, and a crude significance-level vector was calcu-lated as SL(λ) = W (λ)/σ(λ). Initial absorber locationswere identified by finding local maxima in significancelevel where SL ≥ 3. We repeated the procedure usingbroader convolved-Gaussian templates (b = 50 km s−1
or 100 km s−1 typical of broad or blended absorptionlines) although the line-finding technique is not sensitiveto the exact choice of kernel width. Any additional linelocations found with the broad kernels were added to thelist.Next, Voigt profiles convolved with the COS line
spread function were fitted to the normalized data ateach of the identified SL > 3 locations. All lines wereinitially fitted as Lyα, and the fit parameters were al-lowed to vary over the range 8 < b < 150 km s−1,10 < log N (cm−2) < 16.5, and ∆v = ±50 km s−1. Ad-jacent lines were fitted simultaneously. The significancelevel of each fit (Keeney et al. 2012) and a goodness-of-fitparameter similar to a reduced χ2 value were determinedfor each component. (Note that, since noise features arecorrelated between pixels, this is not a formal reducedχ2 measurement.) Highly significant lines (SL ≥ 10)with mediocre fit qualities were refitted with two com-ponents, and the resulting one- and two-component fitswere compared with an F -test (Press et al. 1992). Thetwo-component fit was adopted only if it was better thanthe single-component fit by more than 5σ, chosen empir-ically based on a large sample of blended lines in thedata.
The centroid wavelength, b-value, equivalent width,and column density (with uncertainties) of each featurewere recorded in an initial line list along with the fitsignificance level and χ2. Additionally, the local S/Nper resolution element in the normalized data was deter-mined as S/N ≡ 1/σr where σr is the RMS of continuumpixels when smoothed to a resolution element.In the majority of cases, the fully-automated line-
finding and measurement procedure produced acceptablefits. However, some automated line fits were deemed“pathological”, in cases when the solution representedthe minimum χ2 over the parameter search space, butthe fit was clearly not representative of the absorptionfeature. The most common of these cases were fits toweak lines adjacent to strong features in which saturatedpixels would drive the line profile to a spurious fit. Linemeasurements with χ2 > 1 were presented for manualrefitting during the line identification process below, andcomponents were often added or removed based on qual-itative assessment.The automated line-finding algorithm detected ∼30
very broad (b > 75 km s−1), weak (3-4σ) absorbers someof which are not subjectively obvious in the data butmeet the > 3σ significance criterion. Some of these fea-tures were clearly suspect as regions of ambiguous con-tinuum or statistical fluctuations in the noise and wererejected. However, an objective treatment of absorptionfeatures was one of the main drivers for our automatedline-finding systems and we included most of them in thefinal line catalog. We caution users that they should in-terpret these features with a critical eye in any detailedanalysis.
TABLE 3Galactic Absorption Lines in Far-UV HST/COS Spectra
Strong Lines Weak LinesIon λ (A) f Ref.a Ion λ (A) f Ref.a
NI 1134.17b 0.0146 1 Fe II 1142.37 0.0040 1N I 1134.41b 0.0287 1 Fe II 1143.23 0.0192 1N I 1134.98b 0.0416 1 C I 1157.91 0.0212 1Fe II 1144.94 0.0830 1 C I 1194.00 0.0124 1P II 1152.82 0.245 1 Mn II 1197.18 0.148 2Si II 1190.42 0.292 1 NV 1238.82 0.156 1Si II 1193.29 0.582 1 Mg II 1239.94 0.000621 3N I 1199.55b 0.132 1 Mg II 1240.39 0.000351 3N I 1200.22b 0.0869 1 NV 1242.80 0.0777 1N I 1200.71b 0.0432 1 C I 1277.25 0.1314 4Si III 1206.50 1.63 1 C I 1280.14 0.0481 4H I Lyα 1215.67b 0.4164 1 Ni II 1317.22 0.0571 5S II 1250.58 0.00543 1 C I 1328.83 0.0899 4S II 1253.81 0.0109 1 Cl I 1347.24 0.153 1S II 1259.52 0.0166 1 Ni II 1370.13 0.0588 5Si II 1260.42 1.18 1 Ni II 1454.84 0.0323 6O I 1302.17b 0.0480 1 Ni II 1467.76 0.0099 6Si II 1304.37 0.0863 1 C I 1560.31 0.1315 7C II 1334.53 0.128 1 Fe II 1611.20 0.0014 1C II* 1335.66 0.128 1 C I 1656.93 0.1488 7Si IV 1393.76 0.513 1 Ni II 1703.41 0.0060 6Si IV 1402.77 0.254 1 Ni II 1709.60 0.0324 6Si II 1526.71 0.133 1 Ni II 1741.55 0.0427 6C IV 1548.20 0.1899 1 Ni II 1751.91 0.0277 6C IV 1550.78 0.09475 1Fe II 1608.45 0.0577 1Al II 1670.79 1.74 1
a Vacuum wavelengths and oscillator strength refer-
Jenkins & Tripp (2011)b Geocoronal airglow is often associated with this transition in
addition to Galactic absorption.
8 Danforth et al.
There are ∼ 25 strong absorption lines arising in theMilky Way ISM that present a foreground to every ex-tragalactic absorption spectrum (Figure 1, Table 3). An-other ∼ 25 weaker lines, particularly those of N V, C I,and Ni II, are seen in some of the sight lines and can beconfused with IGM absorption. To characterize these,we performed a simple median-combined coaddition ofall 82 normalized COS AGN spectra in the observed (he-liocentric) frame. This “Galactic foreground” spectrumreaches a very high S/N (∼ 100) and shows the fore-ground lines frequently present in the data. It is avail-able on-line as a MAST high-level science product asdescribed in Section 3. Since the relative strengths, cen-troids, and line widths of these Galactic lines vary fromone sight line to another, the foreground spectrum can-not be used as a true Galactic flat field. However, itis useful for identifying individual absorption features asGalactic or intergalactic in nature and in masking re-gions of spectra which are insensitive to IGM absorptionfeatures.Measured lines that are consistent with ISM features
typically seen in FUV spectra are flagged as probableISM absorption. If these lines are not subsequently re-identified as an IGM line or a blend of IGM and ISMfeatures, they are ignored in the remainder of this analy-sis. Table 3 lists the commonly-seen Galactic absorptionfeatures in the 1135 A < λ < 1800 A spectral range. Wenote that Table 3 is not a comprehensive list of all FUVtransitions of reasonable strength; some lines would bevisible in some spectra but for blending with a stronger,omnipresent ISM line (e.g., the weak C I 1260.74 linewhich, even when other comparable C I lines are seen,will always be blended with strong Si II 1260.42 absorp-tion). High-velocity Galactic absorption present in manydatasets (∆v . 500 km s−1) is flagged by using the sameISM template offset by a constant velocity. ISM linesthat have not been re-identified as IGM lines or blendsare recorded in the line lists for completeness along withmeasured equivalent widths, but no attempt has beenmade to measure accurate column densities or detailedcomponent structure for them.The automatic line-fitting procedure assumed that all
features were Lyα forest lines (statistically, the mostcommon IGM line). However, this is not always the case.Line identification was by far the most time-intensivepart of the process, since automation in this area was notreliable. After pathological fits were corrected and v ≈ 0ISM lines were identified and flagged, the remaining lineswere identified by a variety of means. For sight lines cov-ering a long pathlength (∆z & 0.15), correlated absorp-tion in multiple transitions of a species (e.g., H I Lyα andLyβ) was the most productive technique. For instance,overplotting zLyα = (λ/1215.67A) − 1 versus flux and
zLyβ = (λ/1025.72A) − 1 versus flux shows correlatedabsorption at the redshift of any moderately-strong H I
systems (NHI & 1014 cm−2). Comparisons of Lyβ/Lyγreveal strong H I absorbers at zabs > 0.47 where Lyαhas redshifted past the end of the COS/G160M detec-tor. Occasionally, metal-ion doublet transitions O VI
(1031.93, 1037.64 A) and C IV (1548.20, 1550.77 A) re-vealed IGM systems where H I absorption was weak, notpresent, or blended with other absorption lines. Identi-fying the redshifts of the strongest absorption systems
in this way allows us to identify single-transition metalions (C III λ977.02, Si III λ1206.50, N III λ989.80) andother species in some cases (e.g., C II, Si II, O IV, etc.).FUSE data, where present, were used to help confirmthese correlated absorber systems, but is not otherwisemeasured; see Tilton et al. (2012) for a complete list ofFUSE+HST/STIS IGM absorbers. Lyα line fits wereadjusted as needed and those lines identified as some-thing other than Lyα were refitted manually with theappropriate atomic parameters.Regardless of how it was identified, each extragalactic
system redshift was checked for corresponding absorptionin the transitions most commonly seen in the IGM: H I
Lyα-δ, O VI, Si III/IV, C III/IV, N V; see Table 3 ofDS08 for details. A small number of systems also showedabsorption in additional ions (e.g., C II, Fe II/III, Si II,Ne VIII, O IV, N III, and higher-order Lyman lines).
2.3.1. Line Identification Ambiguities
Because Lyα 1215.67 A is typically the strongest tran-sition in any diffuse IGM absorber, many low-columndensity systems are single-line identifications for whichhigher order Lyman transitions and metals are too weakto be observed. As in our previous papers, we assumethat these are weak Lyα lines unless that assumption isinconsistent with either the source redshift (zabs > zAGN
or zabs < 0), a non-detection upper limit in another tran-sition, or a more plausible identification in another iden-tified system.We have identified all absorption features to the best
of our abilities, but some ambiguities exist. Throughout,we assume that any system with more than one line is un-ambiguous. However, single-line identifications are stillin the majority and, in principle present cases where theline identification is ambiguous. The majority (∼ 75%)of IGM systems consist of single Lyα absorbers with-out any confirming Lyβ or metal-ion detection at thesame redshift. This uncertainty is greatest for the longersight lines (higher zAGN ) since there are more interven-ing systems and more possibilities for unusual metal-ionabsorbers. Short pathlengths offer a more compact Lyαforest with fewer realistic possibilities for line identifica-tion; in such cases, Lyα is usually the only plausible iden-tification. Nevertheless, a few of these single-line systemsmay be metal-line absorption from a different redshift orinstrumental features.At z & 0.47, Lyα shifts beyond the red end of the
COS G160M detector; thus there are 13 zAGN > 0.47sight lines with incomplete coverage of the Lyα for-est. In these sight lines, some weak absorbers identi-fied as Lyα at 0.24 < zabs < 0.47 could instead bestrong Lyβ systems at z > 0.47. For stronger lines,the zabs > 0.47 Lyβ interpretation can be ruled outwith a Lyγ upper limit (i.e., the predicted Lyγ absorberis strong enough that it should appear in the data).However, there are ∼ 40 weak lines in our survey withthis Lyα/Lyβ ambiguity. From the ratio of oscillatorstrengths and rest wavelengths, the column density ratioNLyβ/NLyα = (f λ)Lyα/(f λ)Lyβ = 6.2 for a weak line ofa given observed equivalent width. Since the frequencyof H I absorbers ∂2N (N)/∂N ∂z ∝ N−β where β ≈ 1.6,Lyβ absorbers should make up only 6.2−1.6 = 5% ofthe ambiguous sample or ∼ 2/40 weak Lyα absorbers at0.24 < zabs < 0.47 in 13 sight lines.
HST/COS Survey of the Low-z IGM 9
The inverse case (weak Lyα lines at zabs < 0.47misidentified as Lyβ or Lyγ at zabs > 0.47) should not bepresent in this survey, since we require any zabs > 0.47absorber to be confirmed with detections in at least twotransitions at that redshift (typically Lyβ+Lyγ or metal-ion doublets).
2.3.2. Instrumental Features
As with any instrument, HST/COS suffers from a va-riety of instrumental artifacts which can masquerade asreal spectral features. These include “gain sag”, areasof decreased sensitivity near detector edges, and fixedpattern noise. Use of quality flags in the coadditionprocess described above minimizes the impact of mostof these effects, but some instrumental features are in-evitably present in the data. Fixed pattern noise is themost common feature and manifests itself as small undu-lations across the face of an otherwise smooth continuum.Efforts to fully characterize this noise, much less correctfor it, have proven elusive (Wakker et al. 2015). A smallfraction of our weak absorption features are undoubtedlyinstrumental rather than real features.
2.4. IGM Absorber Analysis
2.4.1. Significance Levels
Absorption features are initially identified using thesimple approximation for significance level as describedabove. During the fitting process, we use a more rigoroussignificance-level formula
SL = (S/N)1Wλ
∆λ
1
w(xopt), (1)
where (S/N)1 is the signal-to-noise ratio per single pixel,Wλ is the equivalent width, and ∆λ is the pixel width inAngstroms. The quantity w(xopt) is an empirical func-tion that describes the correspondence between (S/N)1and the signal-to-noise level for data binned to an op-timal number of pixels xopt. The parameter xopt is afunction of the observed wavelength and the Doppler b-parameter of the feature. Full details are given in equa-tions (4), (7), and (9-11) of Keeney et al. (2012)10.Throughout the analysis, we retain any features mea-
sured with SL > 3. However, even 3σ features are statis-tically common; a typical COS spectrum covers approx-imately 8000 resolution elements between 1150 A and1800 A. There could be ∼ 20 spurious 3σ features withb-values typical of narrow absorbers in each COS/FUVspectrum given normally-distributed (Gaussian) noise.A more stringent 4σ detection threshold should resultin less than one spurious feature per spectrum.The COS noise characteristics are poorly constrained
and are not purely Gaussian in nature (Keeney et al.2012). However, a 4σ detection threshold will still resultin fewer spurious detections than a 3σ criterion; for thisreason, we set the following criteria for inclusion in ourcatalog. Single-line detections must be ≥ 4σ. However,if a 4σ prior exists from another line detection, we relaxthe threshold to ≥ 3σ for lines in other transitions atthe same redshift. For example, a single weak absorber
10 An IDL routine to implement the significancelevel calculation of Keeney et al. (2012) can be found athttp://casa.colorado.edu/∼danforth/science/cos/costools.html .
measured at 3.5σ would be rejected from the statistics,but retained in the line catalog. However, if the weak ab-sorber can be interpreted as a metal line or higher-orderLyman transition of a stronger absorber, it would be ac-cepted and used in later analysis. Practically, this meansthat the Lyα detection threshold is set at 4σ, while thehigher-order Lyman line and metal-line threshold is 3σsince metal absorption is almost never seen without anH I prior. We use the same criteria for calculating thetotal pathlength ∆z(N) observed at column density N .
2.4.2. Intrinsic Absorbers
Galactic features are easily identified as discussedabove. However, absorption from intervening (IGM)gas can sometimes be difficult to differentiate from thatarising from gas associated with the AGN host galaxyitself (intrinsic absorbers with zabs ≈ zAGN). Thereis no definitive way to identify an absorber as one orthe other since AGN outflow features have been ob-served at very large relative velocities. We automati-cally flag any absorber with ∆v = (czAGN − czabs)/(1 +zAGN) ≤ 1500 km s−1 as a possible intrinsic system.The exceptions to the ∆v < 1500 km s−1 proximityrule are the four BLLacertae objects in the sample.These objects (1ES1553+113, 3C66A, S5 0716+714, andPG1424+240) have source redshift lower limits definedby observed narrow IGM absorbers. Since outflowsare sometimes seen at higher velocities, we also man-ually check any metal-line systems at 1500 < ∆v <5000 km s−1 for obvious AGN-intrinsic properties (e.g.,Dunn et al. 2007; Ganguly et al. 2013). We flag as in-trinsic all systems in this redshift range that show strongabsorption in high ions (O VI, Ne VIII, N V, etc.), stronghigh ions but weak H I, strongly non-Gaussian profiles,or doublet equivalent width ratios close to 1:1 (whichmay result from partial covering of the source). Simi-larly, we mask out czabs < 500 km s−1 as possible high-velocity absorption associated with our Galaxy or ab-sorption from the Local Group11. Absorbers in theseregions are measured and reported in the tables, butare not included in IGM statistics, nor are these redshiftranges included in the total IGM pathlength calculations.
2.4.3. Components and Systems
In the following analyses, we classify IGM absorbers intwo ways. First is the self-explanatory component anal-ysis where individual fitted velocity components are an-alyzed as measured. However, it is often more useful todiscuss systems of components (e.g., Tripp et al. 2008)at nearly the same redshift. Systems are composed ofcomponents in the same sight line within a narrow ve-locity window, which allows lines of different species to bedirectly related, even if there are small velocity misalign-ments between them. We adopt this method to managethe ambiguity often present in associating componentsof one ion with components in another (Figure 2) oreven the ambiguity within a given transition composedof multiple, blended components. Detailed analysis of an
11 A subset of COS observations were designed to probe M31,the Magellanic Clouds and Stream, or high-velocity structuresin the Milky Way halo. Thus, we exclude the lowest-velocity(cz ≤ 500 km s−1) regions from our IGM survey to limit “dou-ble counting” of absorbing structures.
Fig. 2.— Absorption in the PG1216+069 sight line illustratesthe distinction between components and systems. Individual com-ponents are denoted with vertical ticks, labeled by redshift, andgrouped together into three absorbing systems (blue horizontalbars). Any components that fall within the system velocity rangeare included in their respective systems. At first glance, the ab-sorbers near z ≈ 0.1237 and z ≈ 0.1247 appear similar. However,the former complex shows consistent velocity structure in all linesso the components can be unambiguously assigned to one or theother of two narrow (∆vsys ≈ 40 km s−1) systems at z = 0.12360or z = 0.12390. The redder system is more ambiguous; while theH I absorption clearly shows two components, the OVI and C IVabsorption shows a single, broad component which cannot be un-ambiguously assigned to either H I component. For this reason,we maintain a single, broader (∆vsys = 115 km s−1) system atz = 0.12474.
individual system may reveal more accurate associations(Savage et al. 2014; Stocke et al. 2014), but this depth ofanalysis is beyond the scope of our large, semi-automatedsurvey. Initially, we defined systems by stepping throughthe components in a sight line in order of line strengthand grouping any other components measured within∆vsys = 30 km s−1 into the same system. Systems withstronger, broader lines used ∆vsys = cWr/λ0 to accountfor ambiguously blended absorption components.The automatic system-definition algorithm accounted
for most strongly-blended absorption components andvelocity calibration uncertainties between different wave-length regions in the data. The majority of systems(1888/2611≈72%) were composed of a single line, and
for these there is no distinction between component andsystem nomenclatures. However, all 723 systems com-posed of more than a single component, whether multi-ple, blended components of the same transition or com-ponents in multiple transitions, were checked manually.In most cases, the automatic system definitions were con-firmed, but the redshifts zsys and velocity widths ∆vsysfor some systems were adjusted manually to account forobvious, unambiguous component structure. Finally, allmeasured components were checked to make sure theyappear in one, and only one, absorbing system. An ex-ample of three systems composed of four components inLyα is shown in Figure 2.Systems tend to be broader when more components
are included: the 435 two- or three-component systemshave a median width close to the nominal minimum∆vsys ≈ 30 km s−1. The 240 IGM systems with 4-9 com-
ponents tend to be broader with ∆vsys = 36+26−6 km s−1.
The 48 richest systems (composed of 10 or more individ-ual components) have a system width of ∆vsys = 49+34
−18
km s−1, and there are only 12 systems with 100 km s−1
< ∆vsys < 300 km s−1.
2.4.4. Consistency Checks
The final step in the creation of a line list is to checkall line measurements and identifications for consistency.Each line list is screened for features identified as IGMat zabs < 0 or zabs ≫ zAGN. Unidentified lines at < 3σare rejected. Unidentified > 4σ features are examinedand identifications are attempted (though see discussionabove regarding unidentified lines). “Orphaned” lines,such as those identified as metal lines or higher-order Ly-man lines but without any other detections at the sameredshift, are flagged and examined.We check all multiplet transitions (H I, O VI, N V,
Si IV, C IV, Ne VIII, etc.) for consistency in columndensity. For instance, if a line is identified as O VI 1032 Aat a particular redshift, the 1038 A O VI transition mustappear as a detection of consistent strength, a blend witha stronger feature, or a non-detection consistent with the3σ minimum equivalent width at the expected locationin the data. Discrepancies are reevaluated manually.H I column density and b-value measurements based
on single Lyman line profiles tend to overestimatethe line width and underestimate the column density(Danforth et al. 2010a). We determine column densityand b-values for H I via a curve of growth (CoG) fit tomultiple Lyman lines. CoG fits are performed for sys-tems at z > 0.1 (where Lyβ is covered in COS data)with NHI > 1014 cm−2 in any individual Lyman line.Any outlying single-line measurements or poor CoG so-lutions are reevaluated manually. In most cases, the CoGH I solution is more accurate than a single-line Lyα mea-surement. However, while the CoG solution to a systemwith multiple velocity components tends to preserve to-tal column density in the lines, the b-value may be artifi-cially broad. We do not attempt CoG solutions to metal-ion lines because most of their absorption lines are notheavily saturated. Neither is there enough fλ contrastbetween transitions for a well-constrained CoG solution.
2.5. Biases and Systematics
HST/COS Survey of the Low-z IGM 11
There should be little bias in the IGM sample due tothe choice of AGN in this study. The sight lines werechosen from a large archive of HST/COS observing pro-grams reflecting a broad range of scientific objectives.The one unifying characteristic of the sight lines is thatthey are toward UV-bright targets. Outside a relativelysmall proximity zone, where the AGN may have undueeffect on the local photoionization, the source luminos-ity has no bearing on the properties of the IGM sample.It can be argued that using UV-bright sources selectsagainst strong H I absorption at any redshift along theline of sight; Lyman Limit systems with logNHI ≥ 17.2are opaque (τ0 > 1) to Lyman continuum photons atλ < (1+zabs)×912 A, but this is only true for zabs & 0.4where the Lyman continuum redshifts sufficient to blocka substantial part of the FUV band. Lyman Limit sys-tems at z < 1 are rare (dN/dz≈ 0.33; Stevans et al.2014; Ribaudo et al. 2011), but our sample includes onesuch case (SBS 1108+560, zLLS = 0.4632). Generally theredshifted Lyman continuum flux is only moderately ab-sorbed (Shull, Stevans, & Danforth 2012; Stevans et al.2014), even in the rare, strong H I systems at higher z.Most of our AGN are at zem < 0.4 where LLSs make nodifference to the observed spectrum in the Lyα forest.There is a small bias in that fifteen of the 82 sight
lines were originally proposed because they probe specificgalaxies near the AGN sight line. Measured covering fac-tor of Lyα absorbers with N > 1013 cm−2 at R < Rvir
are ∼ 80% for L > L∗ and 0.1−L∗ galaxies (Stocke et al.2013). Prochaska et al. (2011) finds even higher cover-ing factors for 0.1− 1L∗ galaxies, statistically consistentwith 100%. Tumlinson et al. (2011) find similarly highcovering factors for O VI around L > L∗ star-forminggalaxies. While Stocke et al. 2013 find that dwarfs havesmaller covering factors (∼ 50% at R < Rvir) it turnsout that that sample was heavily biased by Virgo Clustersightlines. Bordoloi (private communication) finds veryhigh covering factors (> 80%) for Lyα at R < Rvir in hisCOS dwarfs sample. Given the observed high coveringfactor of Lyα absorption detected within ±400 km s−1
of a galaxy velocity, this implies that ∼ 15 metal-lineabsorbers are present in our overall sample due to thisselection bias.Column densities inferred from single, saturated
absorption lines can underestimate the true columndensity as determined via a more robust curves ofgrowth, sometimes dramatically (e.g., the cz = 1590km s−1 H I absorber toward 3C 273 first measured byWeymann et al. (1995) based on a single Lyα absorber,subsequently corrected with a multi-line curve-of-growthby Sembach et al. (2001). For the vast majority ofH I Lyman lines, COS absorption line profiles are well-resolved and our Voigt profile fits should give accuratesingle-line column density measurements even in the caseof moderate saturation in the line core (τ0 = 1 − 3).However, stronger Lyman lines should be conservativelytreated as lower limits on the column density. A line-center optical depth τ0 = 3 corresponds to a transmittedflux of 5%. Given the typical S/N of 20 in the sample,we feel this is a conservative limit on where single-linecolumn densities can be trusted. Of the 5121 IGM ab-sorption lines in the catalog, 579 (11%) show τ0 ≥ 3 (Fig-ure 3). Subdividing by transition, 12% of Lyα absorbers
Fig. 3.— Histogram of fitted line-center optical depth (τ0) for3966 H I Lyman lines (black solid) and 1155 metal-ion lines (greendashed) identified as IGM absorption. Given the typical qualityof the COS spectra (S/N & 20), column density measurementsof mildly-saturated lines (τ0 . 3) should be accurate. However,∼ 13% of Lyman lines are saturated τ0 > 3, so column densitymeasurements of these single lines should be taken as lower limits.Saturated metal lines are much less common: ∼ 5%.
are saturated at τ0 ≥ 3 as are 15% of higher-order Lymanlines. Saturated metal-line absorbers are rare, compris-ing only 5% of the total sample. These are also listed aslower limits in the line lists.Most of our analysis in the remainder of the paper is
based on system-level measurements where NHI is de-termined via a robust curve of growth for stronger ab-sorbers. Lower limits on column density of individuallines should not be an issue. However, we list the indi-vidual column density measurements as lower limits inthe line catalog. There are 118 Lyα absorbers at low-redshift (z < 0.1) where Lyβ is not available for a CoGmeasurement. These systems are among those excludedfrom the uniform sample discussed in Section 2.5.Some science programs were designed to probe indi-
vidual extragalactic objects or near-field structures (theMagellanic Stream, M31). This presents a bias for a fewspecific absorbers but the population of absorbers alongthe sight line as a whole is unbiased. In a few other cases,adjacent sight lines may be sampling the same structuresin absorption, creating a “double-counting” bias. Theclosest pair of sight lines in this survey is separated by9.3′ and three other pairs are separated by less than adegree on the sky. The redshift range over which thesight lines are separated by less than 1 Mpc (where cor-related absorption may be expected) is only ∆z ≈ 0.1 or∼ 0.5% of the total survey pathlength. The overall biason the IGM absorber catalog from sight line selection isvery small.Most of the absorbers in the catalog are relatively nar-
row features (b < 50 km s−1). The median dopplerwidth and ±1σ range for IGM components are b = 33+17
−14
km s−1. Automated line-finding codes search for lines atb = 20, 50, and 100 km s−1, and profile-fitting codes fitfeatures with a parameter range 5 < b < 150 km s−1.However, COS is optimized for the detection of narrowlines, and there is an inherent bias in this catalog towardlines close to the resolution limit. We define a sensitiv-ity as a function of b-value, S(b) ≡ [Wmin(b)/Wmin(b =
12 Danforth et al.
10)]−1 where Wmin(b) is the minimum equivalent widthfor a line of a Doppler parameter b in data of a given S/N .There is a slight dependence of S(b) on observed wave-length, but it is insensitive to S/N . For b = 30 km s−1,we see S(30) = 0.66; COS data are only 2/3 as sensitiveto 30 km s−1 lines as they are to those near the resolu-tion limit of the data (b ∼ 10 km s−1). The sensitivitygets worse for broader lines: S(50) = 0.5, S(100) = 0.35,and S(200) = 0.23. Thus, there is a bias against weak,broad lines in the data, even with accurate knowledgeof the AGN continuum and binning appropriate to theline width. In addition, the continuum fitting routineuses bins of 5-15 A in width, so features broader thanseveral Angstroms (b & 300 km s−1), whether they beactual absorption lines, AGN emission lines, flux calibra-tion uncertainties, or other instrumental features, tend tobe fitted as part of the continuum.The two medium-resolution COS/FUV gratings cover
the Lyα forest for absorbers out to z ≈ 0.47. For weakabsorbers, column density can be determined from mea-surements of the Lyα line alone with reasonable accu-racy. However, H I column density measurements basedon saturated Lyα lines alone tend to underpredict thetrue column density (Shull et al. 2000; Sembach et al.2001; Wakker et al. 2015). CoG fits to multiple H I
transitions tend to give a more realistic NHI value forNHI & 1014 cm−2. In a typical COS/FUV dataset,the Lyβ transition redshifts into the G130M detector atz ≈ 0.1. In order to limit column density uncertaintybetween stronger H I systems at low redshift (measuredfrom Lyα alone) and higher redshift (where multi-lineCoG solutions are used), we define a uniform, redshift-limited subsample of IGM systems: weak H I systems(log NHI < 13.5) are included regardless of redshift, butstronger absorbers (log NHI ≥ 13.5) are included only atzabs ≥ 0.1 where a more accurate, multi-line CoG solu-tion is possible; either Lyα+Lyβ or Lyβ+Lyγ at mini-mum. The limited sample (2256 systems) is only slightlysmaller than the full COS sample (2577 systems) sinceonly the stronger systems at low redshift are excluded.However, this limited sample provides a more uniformanalysis of the distribution of H I absorbers in the low-redshift IGM.Given the huge number of IGM absorber systems
(>2600) and the substantial pathlength (∆z > 20)used for their discovery, the sample variance shouldbe small and adds negligible uncertainty to the Pois-son errors. However, based on a previous jack-knifestyle resampling exercise done for a smaller sample(Penton, Stocke, & Shull 2004), we estimate that cosmicvariance can add significant uncertainty to our sampleat logNHI ≥ 15 (i.e., sub-sample sizes with N . 100absorbers). For order of magnitude sized bins startingat logNHI = 15, 16, and 17, we estimate that cosmicvariance adds (in quadrature) 17%, 33% and 86% to thePoisson errors in those bins. Since metal-line systems arealmost always attached to H I systems, the cosmic vari-ance in those samples is small despite their small num-bers.
3. THE CATALOG
To facilitate further analysis, and as a service tothe community, we present data products as a High-
Level Science Product (HLSP) in a partnership withthe Mikulski Archive for Space Telescope (MAST) athttp://archive.stsci.edu/prepds/igm/. The follow-ing products are included for each of the 82 AGN sightlines in this survey:
• A fully-reduced, coadded spectrum in heliocentricwavelength coordinates as discussed in Section 2in fits format including wavelength, flux, error,and local exposure time vectors. Also included arethe “line-less” error, continuum fit, continuum fituncertainty, and a flag for pixels used in the con-tinuum fit.
• A full list of all absorption features measuredat SL > 3 sorted by observed wavelength inASCII format. Each line list table includes linewavelength, significance level (as described in Sec-tion 2.4), line identity, redshift of the feature,equivalent width and 1σ equivalent width uncer-tainty. Extragalactic lines additionally have listedDoppler b-values and measured logN values, bothwith uncertainties, and the reduced χ2 value of theline fit. Three final columns are used to flag likelyGalactic (ISM) lines, likely AGN intrinsic lines, andthe approximate line-center optical depth τ0.
• A table in ASCII format of extragalactic absorp-tion lines grouped by system redshift as describedin Section 2.4. Only extragalactic absorbing sys-tems at z > 0 are listed. The first two columnsidentify each system by system redshift, zsys, andvelocity half-width, ∆vsys followed by the samecolumns of measured quantities in the line lists andcounts of the number of total lines which comprisethe system and the number of metal lines.
• A multi-page “atlas” showing the reduced data,continuum fit, and identified lines. The first panelof the atlas shows an overview of the entire spec-trum in context with some observational detailsfrom Tables 1 and 2 above. Subsequent panels showthe entire range of COS data in 25A segments withidentified absorption features marked with verticalticks and labeled where possible. Red ticks and la-bels denote z > 0 features with abbreviated line-IDand redshift. Green ticks show v ≈ 0 ISM features.A sample page of the seven-page atlas for 3C 57 isshown in Figure 4.
In addition to the individual sight lines, we provide themedian-combined Galactic foreground spectrum, both infits format and as a multi-page atlas in the same formatas the individual sight line atlases. A portion of thisspectrum is shown in Figure 1.
Fig. 4.— Sample atlas page for 3C 57. Coadded flux and error vectors are shown in black and grey, respectively. Green dashed lineshows the continuum. Each page covers 100 A of the COS spectrum. Red text and ticks denote IGM absorbers (identified by species andredshift). Green ticks show v ≈ 0 ISM features. Blue ticks mark lines with no identification.
14 Danforth et al.
3.1. Erratum
We note that a previous version of the HLSP was re-leased in early 2014. While similar in content to this2016 catalog, this version was based on an earlier datareduction pipeline. A number of instrumental artifactswere included as bone-fide IGM absorption lines. In theintervening period, our data reduction pipeline was im-proved tremendously. The current version of the HLSPshould be largely free of instrumental features. Also, thesignal-to-noise of the coadded data has been improved by∼ 10% in most cases due to better coaddition techniques.Data from the 2014 IGM catalog should be discarded infavor of the current version.
4. RESULTS
We measure 5138 significant IGM absorption compo-nents which comprise 2611 distinct absorbing systemsalong 82 sight lines over total H I redshift pathlength∆z = 21.7. This is the largest catalog of low-z IGM ab-sorption to date and represents three times more sightlines and absorbers than any previous low-redshift IGMabsorber study (Lehner et al. 2007; Danforth & Shull2008; Tripp et al. 2008; Tilton et al. 2012) with greatly-improved sensitivity to weaker absorbers (∆z ≈ 18 forNHI ≈ 1013 cm−2). The catalog is intended to be use-ful in many areas of astrophysics, and many follow-onstudies are planned on specific, more focused areas of re-search (e.g., Shull, Danforth & Tilton 2014, on the IGMmetal evolution). Here, we present some global resultsfrom the catalog. Unless otherwise noted, all statisticsrefer to IGM systems, not individual components.
4.1. H I Absorber Distribution
The normalized frequency of absorbing systems dN/dzand the bivariate distribution of H I absorbers as a func-tion of column density and redshift, ∂2N/∂N ∂z, areimportant quantities in low-z cosmology. Line densityis an important probe of cosmic structure in the lin-ear regime and, in comparison with simulations, yieldsconstraints on density, the spectral energy distributionand intensity of the ionizing background (Kollmeier et al.2014; Shull et al. 2015), the evolution of structures, ther-mal structures, and the processes of galaxy feedback(e.g., Dave & Tripp 2001; Cen & Fang 2006; Dave et al.2010; Smith et al. 2011; Shull, Smith, & Danforth 2012;Kollmeier et al. 2014; Shull et al. 2015).Calculating dN/dz accurately requires both good
counting statistics and knowledge of the pathlength∆z(N) covered in the survey for absorbers of a givencolumn density N . Since sensitivity is neither a constantbetween sightlines nor in different spectral ranges, wemust correct for incompleteness in the weak absorbers bycalculating the effective redshift pathlength, ∆z(Nmin),sensitive to minimum column density, Nmin. The effec-tive absorption redshift pathlength ∆z(N) is calculatedin a manner similar to that used in DS08, and we re-fer the reader there for details. Briefly, we calculate the3σ minimum equivalent width, Wmin(λ), as a function ofwavelength in each spectrum (4σ for Lyα detections asdiscussed above). We use the Galactic foreground COStemplate described above to mask portions of the spec-trum with strong Galactic ISM regions. The Wmin(λ)vector is then translated into Nmin(z) for each ion. Since
many species have line multiplets, the minimum columndensity Nmin(z) is different for different transitions of thesame species. For instance, the O VI 1038 A transition isonly half as sensitive to absorption as the stronger O VI
1032 A line. At zabs = 0.178± 0.003 the stronger transi-tion lies within the strong absorption from the GalacticDLA at 1213-1218 A where we have no sensitivity, butthe weaker O VI line lies in clear continuum at 1223 A.Thus, we can still detect O VI absorption at that redshift,albeit at half the usual sensitivity. As in the line lists,the pathlength at (czAGN − czabs)/(1 + zAGN) ≤ 1500km s−1 and czabs < 500 km s−1 is excluded to limitAGN-intrinsic and Local Group absorbers, respectively.In cases where probable intrinsic absorbers are seen atczabs/(1 + zAGN) > 1500 km s−1 with respect to theAGN, that limit is used instead.
TABLE 4Absorption Pathlengths ∆z and Completeness Limits
Maximum zabs log N completenessSpecies ∆z ∆Xa range 75% 50% 25%
a Co-evolving absorber pathlength (Eq. 2).b In principle, the Lyman continuum redshifts out of the
COS/G160M detector at z = 0.97. However, in practice, H I is
most reliably identified and measured using Lyα or Lyβ which
limits the effective range to z < 0.75.c z-limited, uniform sample described in Section 2.5.
Detection limits as a function of redshift are assem-bled for each species (H I, O VI, C IV, etc.) in each sightline, and the accumulated pathlengths at various lim-iting column densities and redshift ranges are totalled.For strong H I absorbers at any redshift, the total path-length ∆zHI = 21.74, but the survey completeness hasdropped to ≤ 50% at log NHI ≤ 12.93. The detailedeffective redshift path ∆z(N) is used to correct for com-pleteness in our counting statistics to calculate dN/dz.Maximum pathlengths in redshift ∆z and co-evolving ab-sorber pathlength ∆X (Bahcall & Peebles 1969) are re-lated by
∆X =
∫ z2
z1
(1 + z)2
[Ωm(1 + z)3 +ΩΛ]1/2dz, (2)
and listed in Table 4 for H I and seven metal species. Thefourth column shows the approximate redshift range overwhich an ion can be effectively identified in COS/FUVdata. The final three columns show the column densitiesat which the IGM survey is 75%, 50%, and 25% complete.Figure 5 shows the distribution of total H I systems
as a function of column density, together with the ef-fective path length ∆z(NHI) and the differential distri-bution (∂2N/∂ logN∂z) after it is corrected for com-pleteness. Figure 6 shows the same distribution for
HST/COS Survey of the Low-z IGM 15
Fig. 5.— HI detection statistics for the full (0 ≤ zabs < 0.75) sample of 2577 IGM H I systems. Left panel shows the number of systemsN per 0.2 dex column density bin. Error bars are one-sided Poisson uncertainty corresponding to ±1σ. The dashed curve and right-handaxis show the effective pathlength ∆z as a function of column density. The differential absorber frequency ∂2N (N)/∂ logN∂z (Eq. 2) isshown in the middle panel. The right panel shows the integrated system frequency dN (> N)/dz. Power law fits to the differential andcumulative distributions are shown as dashed lines. Equivalent width values for b = 25 km s−1 Lyα lines are shown on the top axis of eachpanel.
Fig. 6.— Same as Fig. 5 but for the “uniform” sample (2256 IGM HI systems); weak systems at all redshifts are used, but strongersystems (log N ≥ 13.5) are only included at zabs ≥ 0.1 where multi-line CoG solutions are available.
the more uniform z-limited sample as described in Sec-tion 2.5. Summing over column density down to a givenNHI, we find the cumulative absorption system frequencydN (> N)/dz, which can be fitted with a power law ofthe form
dN (> N)
dz= C14
(
N
1014 cm−2
)−(β−1)
. (3)
Here, we use the traditional notation of β as the in-dex to the differential distribution d2N/dN dz ∝ N−β;the slopes of the cumulative and differential distribu-tions differ by one. Fitting the differential distribution ofthe uniform z-limited sample of 2256 absorbers, we findβ = 1.65±0.02 for systems in the range 12 ≤ log N ≤ 17with a normalization constant C14 = 25± 1 at the fidu-cial column density of NHI = 1014 cm−2 (correspondingto Wr ≈ 240 mA for b = 25 km s−1). The full sam-ple of 2577 systems has fit parameters β = 1.67 ± 0.01and C14 = 23 ± 1. For ease of comparison with otherobservations and with simulation results, we list columndensity bins, number of absorption systems per bin, red-shift pathlength, and ∂2N/∂N∂z values for the uniformH I subsample in Table 5.
In previous surveys (e.g., Danforth & Shull 2005,2008), we adopted β = 2 as the critical index separat-ing “top-heavy” and “bottom-heavy” scenarios, with to-tal IGM mass dominated by the few high-column den-sity systems or the many lower-column density sys-tems. However, this assumption is simplistic as it ig-nores the dependence on column density of the ion-ization fraction (and metallicity in the case of metalion absorbers). These issues are discussed in detail inShull, Smith, & Danforth (2012) who used simulationsto derive the N -dependence of thermal phases and metal-licities in the IGM.The sample quantities in the COS H I systems are
similar to those found in previous low-z IGM surveys us-ing smaller samples; Tilton et al. (2012) found βHI =1.68 ± 0.03 in a sample of 746 absorbers over path-length ∆zHI = 5.38. Slopes of βHI = 1.65 ± 0.07,1.76±0.06, and 1.73±0.04 were found in smaller Lyα/H I
surveys by Penton, Stocke, & Shull (2004), Lehner et al.(2007), and DS08, respectively. Numerical simulationsalso agree, e.g., βHI = 1.70 at z = 0 from Dave et al.(2010).It has been suggested (Rudie et al. 2013) that a small
break in the power law nature of dN/dz may exist for
16 Danforth et al.
IGM absorbers at z ≈ 2− 3 corresponding to the associ-ation of strong absorbers with galaxy halos and weakerabsorbers with unvirialized intergalactic matter. Wesearched for a fit to a broken power law to the cumu-lative dN/dz in the uniform H I sample with both slopesand the column density of the breakpoint as free param-eters. No significant break is found in our low-z sample.
Measuring the Doppler b parameter of an absorptionsystem is more prone to error than measuring the columndensity due to several systematic effects. The profile-fitting routines used here take into account the instru-mental line spread function (Kriss 2011), but the fittedb is still the quadratic sum of thermal and non-thermalline widths. Unresolved component structure and noisein the line profile can both affect the measured b value ofa system. Profiles fitted to Lyα components are known tosystematically over-estimate the b value (Danforth et al.2010a) so single-transition b measurements should beused with caution. Curve-of-growth fits can provide amore accurate measurement of bHI in absorbers with asimple velocity structure, but it is likely that most strongH I absorbers contain multiple, unresolved velocity com-ponents.We limit these effects by examining the b-value dis-
tribution of only those H I systems fitted with a sin-gle velocity component. Figure 7 shows that the distri-bution of b values for weak, single-component systems(NHI ≤ 1013.5 cm−2) has a median b-value of 29.6± 0.5
Fig. 7.— Distribution of line widths for H I systems in the uni-form sample. To limit broadening introduced by multiple compo-nents, we analyze H I lines fitted with only a single resolved velocitycomponent. The weak (log N ≤ 13.5) and strong (log N > 13.5)samples show median b-values of 29.6 km s−1 and 33.8 km s−1,respectively. The dotted line marks b = 40.6 km s−1, the linewidth corresponding to pure thermal broadening at T = 105 K.The FWHM is 1.67b.
km s−1 and a ±1σ range of 17 − 48 km s−1. Strongersingle-component systems (NHI > 1013.5 cm−2) have alarger median (b = 33.8 ± 0.5 km s−1) though a simi-lar range (20 − 49 km s−1). The difference in distribu-tions suggests that stronger absorbers may include un-resolved velocity components, artificially increasing theline width. However, it may not be significant since,in the uniform sample, strong H I systems are analyzedthrough a multi-transition CoG while the weak systemsare measured via a single Lyα component.Both strong and weak H I line-width distributions have
a long tail toward broader widths. Since the width ofa line is a product of both thermal and non-thermalcontributions, the measured b parameter of an absorp-tion line can set an upper limit on the temperatureof the gas. For Lyα components, T = 105 K corre-sponds to b = 40.6 km s−1 if all broadening is ther-mal in nature. These broad Lyα absorbers (BLAs) canprovide a tracer of WHIM gas independent of metal-licity. The weak and strong samples in Figure 7 showBLA fractions of ∼ 26% and ∼ 28%, respectively. How-ever, we caution that unresolved velocity componentscan contribute non-thermal broadening to a line pro-file, so the BLA fractions quoted above are more real-istically upper limits to their true values. For more dis-cussion, see Richter et al. (2006); Lehner et al. (2007);Danforth et al. (2010a); Savage et al. (2010, 2014). In amore intensive evaluation of 85 O VI components fromthis sample by Savage et al. (2014), 45 of which havewell-aligned H I Lyα components, only 14 componentshave H I and O VI line widths indicative of T > 105 Kgas. Therefore, the fraction of BLAs which unambigu-ously trace WHIM gas is a small fraction of the totalH I+O VI absorber population.
4.2. Metal Absorbers
HST/COS Survey of the Low-z IGM 17
Fig. 8.— Fraction of IGM H I absorbers in which metal ions arealso detected as a function of H I column density. Black points(filled circles) show the fraction of IGM systems with a detec-tion in any metal species, while blue (open circles) and red (opensquares) show the fraction with detections in highly-ionized metals(OVI, NV, C IV, NeVIII) and low-ionization metals (Si II/III/IV,C II/III), respectively. Green diamonds show the fraction of multi-phase systems, i.e., systems with both high- and low-ionizationdetections. The bottom panel shows the relative fraction of high-and low-ionization detections as a function of detections in anymetal ion.
The majority of extragalactic absorption componentsare Lyman-series lines of H I (4234/5138 ≈ 82%), andthe majority of those (2946/4234 ≈ 70%) are Lyα. Met-als are detected in 418 IGM systems or ∼ 15% of allIGM systems. This fraction is strongly dependent on H I
column density (Figure 8). For weak H I systems with12.5 ≤ logNHI ≤ 13.5, metals appear in only 48/1465(∼ 3%). For systems 13.5 ≤ log NHI < 14.5, the metalfraction rises to 177/818 (∼ 22%). For the strongest IGMsystems (logNHI ≥ 14.5), metals are nearly ubiquitous(157/212≈ 74%). Two effects probably combine to pro-duce the steeply rising metal fraction seen in Figure 8.First is the finite sensitivity of COS spectra to weak ab-sorbers; metal lines in the IGM tend to be weaker thantheir corresponding Lyα absorbers and thus may not beabove the detection limit for weak H I lines. Second,weak absorbers are often associated with the diffuse IGMfar from galaxies where metallicity is likely to be verylow, while strong absorbers are often associated withhigher-metallicity galaxy halos. The 50% “cross-over”point for metal-bearing absorbers is log NHI ≈ 14.5, thesame column density where∼ 50% of absorbers are foundwithin one virial radius of a galaxy (Stocke et al. 2014).If we subdivide the metal systems into highly-ionized
species (O VI, N V, C IV) and low-ionization species(Si II/III/IV, C II/III), we see that systems with high-ionized metals are relatively more common than low-ionization metals at logNHI < 14, while they becomecomparable in stronger H I systems (Figure 8, bottompanel). This is easily explained by recognizing that H I
is, itself, a low-ionization species. Systems in which O VI
and other highly-ionized metals can exist will have alower neutral fraction and thus lower NHI for a giventotal column density.The most common metal detection is in one or both
lines of the O VI doublet (1031.93, 1037.64 A) whichappear in 280 IGM systems. Lithium-like doublet tran-sitions of C IV (70 systems) and N V (59 systems)are also common. Absorption in the strong, singletlines of Si III (λ1206, 123 systems) and C III (λ977,115 systems) and doublet Si IV (λλ1393, 1402; 45 sys-tems) trace moderately-ionized IGM. In addition, tran-sitions of twenty other metal ions are observed in verysmall numbers; the total census of metal-ion species seenin IGM absorbers is O I/II/III/IV/VI, N II/III/IV/V,C II/III/IV, Si II/III/IV, S IV/V/VI, Fe II, NeVIII, P II,and Al II. We discuss the statistics of the more com-mon metal-ions below. A comparison of these withhigher-redshift surveys in the literature is presented inShull, Danforth & Tilton (2014).
4.2.1. O VI
The O VI 1031.93, 1037.62 A doublet is a strong(f = 0.1325, 0.0658), resonance (2s − 2p) transitionof lithium-like oxygen. O VI receives a great dealof attention not only because it is the most com-monly seen metal ion in the low-redshift IGM andCGM, but because it is thought to be a tracer ofthe warm-hot intergalactic medium (WHIM) at T >105 K (Tripp et al. 2001; Danforth & Shull 2005, 2008;Thom & Chen 2008; Danforth 2009). High-qualityCOS observations of O VI and H I absorbers havebeen used to constrain the physical properties in anumber of IGM systems already (Savage et al. 2010,2014; Stocke et al. 2014; Muzahid et al. 2015). Galaxyredshift surveys around IGM sight lines (Stocke et al.2006; Chen & Mulchaey 2009; Prochaska et al. 2011;Johnson, Chen, & Mulchaey 2015) have shown that O VI
systems are found . 1 Mpc from the nearest L∗ galax-ies and thus closer to galaxies than typical Lyα forestlines. Studies of strong O VI systems (logNOVI & 14.3)in low-S/N COS data (e.g. the COS-Halos projectTumlinson et al. 2011, 2013) have shown a strong cor-relation between the specific star formation rate and thepresence of O VI.O VI absorption is detected in COS/FUV data at
0.1 . z . 0.74 because the G130M grating is only sensi-tive at λ & 1135 A and G160M ends at 1796 A. We find280 O VI systems out of a total of 1658 IGM systemsin this redshift range (∼ 17%). This is a factor of twomore O VI systems than the Tilton et al. (2012) synthe-sis of STIS and FUSE surveys, enabling us to addressthe low-z O VI properties with more statistical rigor.Observational O VI results are shown in Figure 9. Forcomparison, we include equivalent values derived fromthe Tilton et al. (2012) absorber list. In this and all sub-sequent plots that show absorbers from that study, wehave combined all components within 30 km s−1 of eachother to create absorption systems analogous to those weuse in the COS survey.The left panel of Figure 10 shows that O VI and H I
column densities are essentially uncorrelated. What’smore, we see O VI systems ranging over a factor of
18 Danforth et al.
Fig. 9.— Statistics of 280 OVI systems in the COS IGM survey. The left panel shows the number NOVI of absorbers per 0.2 dex columndensity bin. Error bars are one-sided Poisson uncertainties corresponding to ±1σ. Completeness is shown in the form of total pathlength∆z(NOVI) (dashed blue line, right axis). Approximate equivalent width in the stronger line of the doublet is given on the top axis. Themiddle panel shows the differential frequency of absorbing systems ∂2N/∂ logN∂z. Open, red points show the corresponding distributionin the FUSE+STIS sample presented in Tilton et al. (2012) but post-processed to use the same convention of systems we use here. Rightpanel shows the cumulative frequency dN (> N)/dz. A single power law clearly does not represent the data, but a broken power law (reddashed line) with parameters βstrong = 2.5± 0.2, βweak = 0.56 ± 0.16, log Nbreak = 14.0 ± 0.1, Cbreak = 9.9 ± 1.4 provides a statisticallybetter fit. A distribution from a cosmological simulation Smith et al. (2011) is shown as a blue dotted line in the right panel.
Fig. 10.— Column densities of H I and OVI are poorly correlated (left panel). Green triangles show the absorbers from the COS-Halossurvey (Tumlinson et al. 2011). The fact that COS-Halos points occupy only one region of the larger parameter space suggests that not allOVI absorption is associated with the halos of L∗ galaxies. The multiphase ratio NHI/NOVI in the right panel as a function of NHI showsthe poor column density correlation between neutral and highly-ionized systems which are kinematically related. Dashed lines show valuesof constant NOVI and NHI. A power-law fit to the data (red dotted line) has index 0.86± 0.01 showing that the two column densities arepoorly correlated. Components with NOVI . 1013 cm−2 are not typically detectable at our survey S/N.
∼ 100 in column density (13 . logNOVI < 15) whilethe H I column density varies by a factor of 106 inthis survey (12 < logNHI < 18) but has been observedat much higher column densities in other extragalacticcontexts. This lack of correlation has been noted be-fore (Danforth & Shull 2005) where we defined a multi-phase ratio, NHI/NOVI, to track the relative amounts ofwarm, photoionized and highly-ionized gas which oftenare kinematically associated. The right panel of Fig-ure 10 shows the multiphase ratio as a function of H I
column density. Because of S/N limitations, we are notsensitive to the weakest systems, which imposes approx-imate sensitivity limits N & 1013 cm−2 for both O VI
and H I (upper dashed line in Figure 10). However, wesee a sharp lower bound to the multiphase ratio, im-plying a maximum O VI column density of a few times1014 cm−2 which is only weakly correlated with NHI. A
fit to the multiphase ratio (dotted line) shows the rela-tionship NHI/NOVI ∝ (NHI/10
14 cm−2)0.86±0.01. Thisslope is consistent with that of Danforth & Shull (2005)who found NHI/NOVI ∝ N0.9±0.1
HI .Green triangles in Figure 10 show the equiva-
lent measurements drawn from the COS-Halos survey(Tumlinson et al. 2011). These are absorbing systemsassociated with the halos of large (> L∗) star-forminggalaxies. The COS-Halos surveys were not as sensitiveto column densities of H I or O VI as is the current survey,but it is apparent that most star-forming galaxies can beassociated with O VI absorption. However Stocke et al.(2006) find strong O VI absorbers at impact parametersout to ∼ 0.8 Mpc which is 2−3 Rvir for L > L∗ galaxies.So, while L∗ galaxies have strong O VI absorption, theconverse is not necessarily true.Calculating dN/dz for O VI in the same manner as we
HST/COS Survey of the Low-z IGM 19
Fig. 11.— Same as Figure 9 but for 59 NV systems (top panels) and 70 C IV systems (bottom panels).
used in Section 4.1 for H I, we see a distinct “knee” inthe distribution at log N ≈ 14 (Figure 9, right panel).A broken power-law fit to the cumulative distributionof O VI absorbers (dashed curve) shows a break atlog N = 13.9 ± 0.1 with indices βweak = 0.55 ± 0.16and βstrong = 2.5± 0.2 and a normalization at the breakof Cbreak = 9.7 ± 1.3. There is curvature in the distri-bution not accounted for in the fit. A power-law fit isprobably not the most appropriate in this context, butwe present it here for comparison with theory and pre-vious observational work. This two-slope distribution isin marked contrast with the results of DS08 who fittedthe entire distribution (comprised of only 83 O VI ab-sorbers) with a single power-law with β ∼ 2.0 over therange 13.2 < log NOVI < 14.8.We note that the slope break at logN ≈ 14 is
the dividing line of the population of circumgalacticO VI absorbers seen in the large COS-HALOS project.Tumlinson et al. (2011) find a strong correlation be-tween star-forming galaxy halos and O VI absorption atlogNOVI & 14.2, while passive red galaxies tend to haveO VI upper limits at this level or below. The weaker sys-tems seen in this and previous surveys may correspondto more diffuse gas, possibly intra-group (Stocke et al.2014) or a true intergalactic medium. “Circumgalactic”O VI gas may lie outside the virial radius of galaxies andstill be flowing out to enrich the IGM (Stocke et al. 2006,2013; Shull 2014).
4.2.2. C IV and N V
O VI, N V and C IV are all lithium-like tracers ofhighly-ionized gas, which is either collisionally ionized toT & 105 K or photoionized by photons with ∼ 50− 100eV. Due to the lower cosmic abundances of carbon andnitrogen, as well as the limited redshift range over whichC IV can be observed in COS/FUV spectra (zabs . 0.16),the number of IGM systems in which these ions are ob-served (N V: 59 systems, C IV: 70 systems) is consid-erably smaller than the 280 detected in O VI. Neverthe-less, this is larger than previous surveys of these two ionsin the low-redshift IGM. Both species appear to followsimilar distributions to O VI, though without as muchsensitivity to the weaker absorbers (Figure 11). The N V
distribution is consistent with a single power law with in-dex β = 2.2± 0.1 and normalization C14 = 0.3. The cu-mulative distribution of C IV shows a clear turnover andis fitted with a broken power law (βweak = 1.4± 0.3 andβstrong = 2.1±0.3) with a break at log NCIV = 13.5±0.2and normalization Cbreak = 5.5± 1.5.
4.2.3. Si III, C III, and Si IV
Si III and C III absorbers are relatively common inthe IGM owing to the very high oscillator strengths ofthe 1206.5 A (f = 1.63) and 977.0 A (f = 0.757) tran-sitions, respectively. These systems in previous studiestrace H I systems to a much better extent than morehighly-ionized species, probably because C III and Si IIItrace photoionized gas. We detect Si III and/or C III
absorption in 200 systems. The Si IV doublet (λ1393.76,1402.70) is also relatively strong, though the decreasedabundance of Si compared with cosmic C/N/O means
20 Danforth et al.
Fig. 12.— Same as Figure 9 for 123 Si III systems (top panels), 115 C III systems (middle panels), and 45 Si IV systems (bottom panels).
that these absorbers are somewhat less common; only 45Si IV systems are seen in this survey for a line frequencydN/dz ∼ 3 for components with Wλ & 30 mA.Si III, with an ionization range of 16.3-30.7 eV, traces
low-ionization, metal-enriched gas, while C III (24.4-47.9eV) and Si IV (33.5-45.1 eV) trace an ionization middle-ground between Si III and the high-ionization speciessuch as C IV, N V, and O VI (50-150 eV). The dN/dz dis-tribution of all three lower-ionization species is consistentwith that seen in previous surveys (Figure 12). The Si IVdistribution is fitted with β = 1.9 ± 0.2, C14 = 0.2 forlog NSiIV ≤ 14.1. C III is reasonably fit with a two-slopepower law (βweak = 1.4 ± 0.1 and βstrong = 1.81 ± 0.1)with a break at log Nbreak = 13.3 ± 0.3 and normaliza-tion at the break of Cbreak = 5.9 ± 1.6. Si III does notfollow an obvious power-law distribution.
4.2.4. Are the dN/dz turnovers real?
The distributions of many of the metal ions shown inFigures 9-12, particularly O VI, C IV, and C III, show aclear flattening in their numbers at lower column densi-ties. Because weaker systems are observed over smallereffective pathlengths, we investigated whether this ap-parent flattening in the dN/dz values at lower columndensities could be an effect of completeness. The nominal∆z(N) function (e.g., blue curve in Fig. 9) is calculatedfor 3σ detections. If this threshold is raised to 4σ oreven 5σ, there is relatively less effective pathlength atlower column densities. The blue curve in the left-handpanels of Figures 9, 11, 12 representing ∆z(N) movesto the right by ∼ 0.1 dex for each 1σ increase in signif-icance level and the frequency of weak absorbers rises.This results in a steepening of the weak-end slope of the
HST/COS Survey of the Low-z IGM 21
cumulative distribution function (less pathlength for thesame number of detections), but it does not eliminate thenon-power-law nature of the distributions in any case.Therefore, we believe that the turnovers seen in many ofthe metal absorber distributions are real, but the weak-end slopes should be treated with caution.
4.2.5. Ne VIII
The extreme-UV (2s-2p) doublet transition of Ne VIII
(λ = 770.41, 780.32 A, f = 0.1030, 0.0505) is often pre-sented as a less-ambiguous tracer of collisionally-ionizedgas than O VI or other highly-ionized, FUV metal tran-sitions (Savage et al. 2005). Several compelling studiesof warm-hot or multi-phase gas have been made usingNe VIII in conjunction with H I, O VI, and other ionsto constrain gas temperature and density (Savage et al.2005, 2011; Narayanan et al. 2009, 2011; Tripp et al.2011; Meiring et al. 2013; Hussain et al. 2015). Unfor-tunately, the Ne VIII doublet only redshifts into theCOS/G130M band at z & 0.47, which limits us to the13 highest-redshift AGN sight lines in this survey, usu-ally without observations of the strong H I counterparts.We measure several dozen Ne VIII systems, but all areat redshifts similar to the background AGN and showthe hallmarks of intrinsic, rather than intervening, ab-sorption. They are primarily strong, blended, multi-component absorption profiles in high ions, often lackingH I absorption.There are three Ne VIII detections at > 3σ in our sur-
vey which can be reasonably identified as intergalacticrather than intrinsic systems. The system detected to-ward PKS0405−123 at z = 0.49494 in Ne VIII and O VI
by Narayanan et al. (2011) shows logNNeVIII = 13.5±0.2and logNOVI = 14.32± 0.05. In an earlier reduction ofthe data allowing for the contaminating effects of fixedpattern noise, Narayanan et al. (2011) measured columndensities of logNNeVIII = 13.96 ± 0.06 and logNOVI =14.39±0.01. The origin of the Ne VIII and O VI is consis-tent with collisionally ionized gas with T ≈ 5×105 K anda baryonic column density of NH ∼ 1019 − 1020 cm−2.Two other Ne VIII detections have no correspond-
ing absorption in any other metal or H I transition.The first is a pair of broad absorption features towardPKS0637−752 consistent with Ne VIII doublet absorp-tion at z = 0.60552 with log NNeVIII = 14.0. Lyβ andO VI λ1032 non-detections place 3σ column density up-per limits of log NHI ≤ 13.6 and log NOVI ≤ 13.4, respec-tively. A similar pair of weak absorption features in theHE0238−1904 sight line is consistent with a z = 0.50511Ne VIII doublet: log NNeVIII = 14.1, log NHI ≤ 13.4,and log NOVI ≤ 13.2. These two Ne VIII-only systemsprovide interesting limits on the temperature of the ab-sorbing gas. If in collisional ionization equilibrium (CIE)and with the solar Ne/O abundance, the O VI non-detections imply a gas temperature of T & 3 × 106 K.This is consistent with the broad line profiles observedin the Ne VIII lines (bthermal ∼ 50 km s−1) and the H I
non-detection.The effective pathlength for Ne VIII systems with
NNeVIII ≈ 1014 cm−2 in our survey is only ∆z ∼ 2,quite small compared with the other species presentedhere. With only three significant detections over thispathlength, only one of which is confirmed with absorp-
tion in other species, it is clear that Ne VIII absorbersdetectable in data of modest S/N are rare. Based on thedetection of three Ne VIII/O VI systems in the high-S/Nspectrum of PG1148+549 (zem = 0.9754), Meiring et al.(2013) estimate dN/dz = 7+7
−3 for Ne VIII systems with
Wr > 30 mA. Our modest-S/N finds dN/dz ∼ 3 withlarge uncertainties for Ne VIII systems with Wr > 30mA. Effective surveys for a statistical sample of Ne VIII
systems will require high-S/N observations of AGN atredshifts of z ∼ 1 or a deeper survey at slightly lowerredshifts (zNeVIII & 0.41) with the COS G130M/1222setting.
4.3. Baryon Census
The majority of baryons at all epochs arenot in the form of virialized, luminous matter(Shull, Smith, & Danforth 2012). Thus observations ofthe diffuse IGM are the most effective way of trackingthe majority of normal matter across the history ofthe universe. As in previous papers (Danforth & Shull2005, 2008; Tilton et al. 2012), we calculate the baryoncontent of the IGM systems, here observed with COS.We compute two quantities: Ωion, the contribution toclosure density by a particular element and ionization
stage; and Ω(ion)IGM which estimates the fraction of closure
density represented by all the gas traced by absorptionin a particular species. Ωion is a purely-observational
quantity with no corrections, while Ω(ion)IGM must include
assumptions about metallicity (Z/Z⊙), solar elementalabundance (M/H)⊙, and ion fraction (fion).A full discussion of our methodology is presented in
Section 2.4.1 of Tilton et al. (2012) from which we useEq. (5) to calculate Ωion
Ωion=(1.365× 10−23 cm2)h−170 (mion/amu)×
logNmax∑
i=logNmin
[
∂2N (logN)
∂ logN ∂z
]
i
〈Ni〉∆ logNi. (4)
For metal ions, we calculate Ω(ion)IGM via Eq. (6) of
Tilton et al. (2012)
Ω(ion)IGM =
1.83× 10−23h−170 cm
2
fion(Z/Z⊙) (M/H)⊙×
Nmax∑
i=Nmin
[
∂2N (logN)
∂ logN ∂z
]
i
〈Ni〉∆ logNi. (5)
Instead of assuming a constant metallicity Z and ionfraction fion as we have done in previous calcula-tions (Penton, Stocke, & Shull 2004; Danforth & Shull2005, 2008), we take advantage of the covariance ofthe product fion (Z/Z⊙) seen in in cosmological sim-ulations. We used parameterized fits of the formfion (Z/Z⊙) = A (Nion/10
14 cm−2)B. Parametriccoefficients for the species are as follows: O VI:A=0.015, B=0.700 (Shull, Smith, & Danforth 2012);N V: A=0.036, B=0.617; C IV: A=0.009, B=0.690 (B.Smith, 2013, priv. comm.).The baryon fraction traced by photoionized H I ab-
sorbers in the Lyα forest Ω(HI)IGM is calculated via Eq. (10)
22 Danforth et al.
of Tilton et al. (2012)
Ω(HI)IGM(z)= (9.0× 10−5)h−1
70 p1/2100T
0.3634.3 ×
(1 + z)0.2
[Ωm(1 + z)3 +ΩΛ]1/2×
Nmax∑
i=Nmin
[
∂2N (logN)
∂ logN ∂z
]
i
∆ logNi
(
〈Ni〉
1014 cm−2
)1/2
(6)
where the temperature T is normalized at 20,000 K,column density is in units of 1014 cm−2, and theAGN sight line impact parameter p100 is normalized to100 kpc. See full discussion in Tilton et al. (2012) andShull, Smith, & Danforth (2012).
We calculate Ωion and Ω(ion)IGM values for H I, O VI,
N V and C IV (Table 6). Uncertainties are the per-bin errors added in quadrature. The dominant sourceof random error for all Ω calculations is small num-ber statistics. For this reason we sum over the col-umn density range of typical, weak and moderate lines(NHI < 1016 cm−2, Nmetal < 1015 cm−2) and do notinclude the rare, high-column density absorbers. These
may add an additional 6− 8% to Ω(HI)b . The lower limit
to the column density range is observationally motivatedand corresponds to Wr = 30 mA. The sample of H I
absorbers is large enough to divide the sample into red-shift bins of ∆z ≈ 0.1 and maintain reasonable statis-tics. Metal ions lack sufficient detections to subdividethe same way, and thus Ωion and Ω
(ion)IGM for each metal
ion are calculated over a single redshift range.The Ωb analysis finds that the photoionized Lyα for-
est at z . 0.5 can account for ∼ 20 − 25% of thebaryons while WHIM gas as traced by O VI can ac-count for ∼ 11%. These fractions are smaller than thosefound in our previous surveys (Penton, Stocke, & Shull2004; Danforth & Shull 2005, 2008) since we use themore realistic ionization corrections and fion (Z/Z⊙)method described above while previous surveys used a
constant metallicity Z/Z⊙ = 10% and an ionizationfraction near the peak of the CIE abundance for thation. However, these results are consistent with those ofTilton et al. (2012) and Shull, Danforth & Tilton (2014)both of which apply the same covariance technique ap-plied here to the STIS and FUSE data of Tilton et al.(2012). For a more detailed comparison of the evolu-tion of Ωmetal in the IGM with previous work includ-ing higher-redshift studies (see Shull, Danforth & Tilton2014).
4.4. Metal-ion Abundances
Over the past two decades, there have been numer-ous studies of the metallicity evolution of the IGM,probed by the strong metal-ion absorbers in the rest-frame ultraviolet (C IV, Si IV, O VI, and a few oth-ers). These metal abundances are parameterized bytheir densities relative to the cosmological closure den-sity, for example ΩCIV . Shull, Danforth & Tilton (2014)discussed the metal-line measurements from 49 low-redshift (z < 0.4) HST/COS absorbers to find ΩCIV =10.1+5.6
−2.4 × 10−8 (h−170 . In their Table 2, they compare
this value to updated measurements from HST/STIS(Tilton et al. 2012). The Tilton et al. (2012) num-ber was revised by Shull, Danforth & Tilton (2014) toΩCIV = 8.1+4.6
−1.7 × 10−8 h−170 which is not statistically
different from the COS measurement. The adjustmentswere based on three corrections to the method: (1) com-putation of absorbers over a consistent range of columndensity (12.87 < logNCIV < 14.87); (2) re-evaluation ofthe absorber sensitivity and effective redshift pathlength;(3) fitting the distribution f(N, z) to an analytic formand integrating over the assumed column-density range.The first restriction eliminated a few strong, uncertainabsorbers that biased the computation. The second andthird adjustments resulted in a more robust computa-tion, as discussed extensively in Section 2.2 and Table 2of Shull, Danforth & Tilton (2014). There is now generalagreement on the low-redshift value for the C IV metalabundance, ΩCIV ∼ 10−7 (H70)
−1.
TABLE 6Ω Values
Species logNa zabs Nabs Ωion Ω(ion)IGM Ω
(ion)IGM /Ωb
b
(×10−8) (×10−3) (%)
H I 12.8− 16 0− 0.1 801 18.01+2.49−2.06 7.70+0.43
−0.38 16.9+0.9−0.8
H I 12.8− 16 0.1− 0.2 660 24.14+4.21−3.36 8.82+0.60
−0.51 19.4+1.3−1.1
H I 12.8− 16 0.2− 0.3 369 20.89+4.69−3.49 8.14+0.71
−0.58 17.9+1.6−1.3
H I 12.8− 16 0.3− 0.4 262 32.95+7.34−5.59 11.66+1.18
−0.95 25.6+2.6−2.1
H I 12.8− 16 0.4− 0.47 140 40.47+14.66−9.80 14.19+2.32
−1.66 31.2+5.1−3.7
H I 12.8− 16 0− 0.4 2092 24.00+4.99−3.84 9.08+0.78
−0.64 20.0+1.7−1.4
OVI 13.4− 15 0.1− 0.75 255 40.37+4.54−3.07 4.96+0.37
−0.31 10.9+0.8−0.7
NV 13.2− 15 0− 0.45 46 2.10+0.53−0.35 1.57+0.32
−0.23 3.4+0.7−0.5
C IV 12.9− 15 0− 0.16 68 9.89+3.24−1.73 3.63+0.71
−0.45 8.0+1.6−1.0
a Additional baryons may be present outside the range (12.8 ≤
The COS IGM survey samples a large fraction of thehistory of the universe with a statistically-significantnumber of absorbers seen along many sight lines. OurLyα forest sensitivity with the COS G130M/G160Mgratings ends at z ≈ 0.47, equivalent to a lookback timeof 4.9 Gyr or 35% of the age of the universe, so it is logicalto look for changes in the overall sample properties (suchas dN/dz) over that time. The sight lines are biased to-ward low-redshift AGN targets (median zAGN = 0.19)and the absorbers are similarly biased toward lower red-shifts (the median and ±1σ redshift sensitivity of oursurvey is zabs = 0.14+0.19
−0.10). We calculate effective path-length as a function of column density as above, but useonly the portion of each sight line which probes a partic-ular redshift range. A limit and final point are includedin Figure 13 the bin at 0.47 < z < 0.75 in which sys-tems are found via Lyβ and Lyγ absorption. However,the uncertainties in pathlength for this range, as well asthe small sample size in this redshift bin, make any firmconclusions beyond z ≈ 0.47 difficult.The left panel of Figure 13 shows how the observed
dN/dz changes as a function of redshift in our sample.We fit the cumulative frequency of lines above a cer-tain column density as dN (> N, z)/dz = C0 (1 + z)γ .The strong sample (NHI > 1014 cm−2) shows clear evo-lution with γ = 2.3 ± 0.1, C0 = 16 ± 1. Evolution inthe weaker sample (1013 cm−2< NHI < 1014 cm−2) canbe fitted with γ = 1.15 ± 0.05, C0 = 74 ± 1, but thereis an increase in weak systems at z & 0.3, which sug-gests that a simple power law may not be appropriate forweak system evolution. The observed γ > 0 means thatthe frequency of IGM absorbers is lower at z = 0 thanat higher redshift, while the difference ∆γ ≈ 1 betweenstrong and weak systems means that weaker systems be-come relatively more dominant at lower redshifts. A fitto the NHI ≥ 1013 cm−2 sample gives γ = 1.24 ± 0.06and C0 = 91± 1.The difference in evolution indices γ for strong and
weak H I systems implies that the slope β of the dN/dzdistribution should become steeper with decreasing z.This is observed in the data as well (right panel of Fig-ure 13). The evolution at z ≤ 0.47 of systems in therange 13 ≤ logNHI < 17 is fitted by β(z) = (1.70 ±0.01)− (0.15± 0.06) z. However, given the heterogenousmethod of H I column density determination betweenthe z < 0.1 and z > 0.1 systems, we prefer the steeper fitβ(z) = (1.75± 0.03)− (0.31± 0.10) z (dashed red line).In a comparable set of IGM observations at with
HST/STIS in the near-UV, Janknecht et al. (2006) mea-sured β = 1.60±0.03 for IGM absorbers at 0.5 < z < 1.9.Rudie et al. (2013) find β = 1.65± 0.02 for logN > 13.5H I absorbers at z ∼ 2 − 3. However, they note thatthe slope is shallower (β = 1.447 ± 0.033) for absorberswithin 700 km s−1 of a galaxy, which they interpret ascircumgalactic gas rather than IGM. Extrapolating thelow-z fit to the redshifts of the comparable near-UV andoptical studies produces slopes much shallower than areobserved; β(z = 1) ≈ 1.4, β(z = 2.5) ≈ 1.0. Thus,our linear relationship should not be extrapolated to red-shifts beyond z ≈ 0.5.
We can now modify Eq. (3) to
dN (> N, z)
dz= C0 (1 + z)γ
(
N
1014 cm−2
)−[β(z)−1]
. (7)
Here we adopt C14,0 = 16 and γ = 2.3, and the secondfit, β(z) = 1.75 − 0.31 z, which predicts stronger evolu-tion. Theoretically, we would expect the Lyα forest toevolve with redshift due to the rapid drop in photoioniz-ing background at z < 2 and the evolution of density andmass in the large-scale structure of the gaseous filaments.Because the H I neutral fraction depends on the ratioof ionizing flux to density (the photoionization parame-ter U), the H I fraction will evolve in redshift, and thecolumn-density distribution will shift (Dave et al. 2010;Smith et al. 2011; Penton, Stocke, & Shull 2004).
5.1.1. The Lyα Decrement
Another way of assessing the evolution of the Lyα for-est is to measure the Lyα decrement DA(z),
DA(z) =
∑
i
Wr,i (1 + zi)
λ0 ∆zi(z), (8)
the fraction of light removed from the continuum by theLyα forest at any given redshift. At high redshift, thedecrement approaches 100%, but the modern universe ismuch more transparent to light at 1215.67 A. Measuringthe Lyα decrement from the COS survey is a relativelysimple matter. Each absorption component has a mea-sured rest equivalent width Wr,i. Summing the observedequivalent widths Wobs = Wr (1 + z), of all the Lyα ab-sorption components in a particular redshift range andthen dividing by total clear path length ∆zi gives thedecrement (Eq. 8).The Lyα decrement is dominated by H I absorbers of
column density 13.5 < logNHI < 14.5 which are bothcommon and relatively strong. Figure 14 shows thedecrement in ∆z = 0.01 bins (small open circles) andlarger ∆z = 0.05 bins (filled circles). There is consider-able variation from one redshift bin to the next, but thetypical decrement is a few percent. The larger spread indata points at z & 0.35 is probably due to the increasingeffects of cosmic variance as a result of the smaller num-ber of sight lines probing this redshift range. A power lawfit to the data gives DA = (0.014 ± 0.001) (1 + z)2.2±0.2
and DA = (0.013±0.001) (1+z)2.1±0.2 for the ∆z = 0.05and ∆z = 0.01 binnings, respectively. This is sig-nificantly steeper than the fit of Kirkman et al. (2007)who used a pixel-optical-depth technique with data fromHST/FOS to find DA = 0.016 (1 + z)1.01.The distribution of DA(z) has been used as a con-
straint on the ionizing radiation field at redshifts z < 0.4(Shull et al. 2015). For absorbers that evolve in line fre-quency as (1 + z)γ , the flux decrement should increasewith redshift as DA ∝ (1 + z)γ+1. The extra factorof (1 + z) arises from the fact that the absorption-lineequivalent width Wλ increases with (1 + z). We observeDA ∝ (1 + z)2.1−2.2, which is consistent with our indexγ = 1.17± 0.06 for unsaturated lines (13 < logN < 14).
5.1.2. Metal Evolution
The sample of metal-line systems is much smaller thanthe IGM H I sample, but we can still use it to constrain
24 Danforth et al.
Fig. 13.— Evolution in the Lyα forest. Left panel: integrated dN/dz values for all (NHI > 1013 cm−2, filled circles), strong (NHI >1014 cm−2, filled squares), and weak (1013 cm−2 ≤ NHI < 1014 cm−2, open squares) H I systems as a function of redshift. The evolution ofthe stronger systems can be fitted by the relationship dN (> N, z)/dz = C0 (1+z)γ with γstrong = 2.3±0.1, C0,strong = 16±1 (blue dashedline). The total and weaker distributions are fitted with γall = 1.24± 0.04, C0,all = 91± 1 (red) and γweak = 1.15± 0.06, C0,weak = 74± 1(black). Vertical dotted lines show the redshift limits of the Lyα and Lyβ forests which can be observed in COS/FUV data. Right panel:power law index β(z) as a function of redshift. A linear fit to the 0.1 < zabs < 0.47 bins (dashed red line) shows a steep evolution ofβ(z) = (1.75 ± 0.03) − (0.31 ± 0.10) z. A high-redshift (0.47 < z < 0.75, green circle) data point based on Lyβ+Lyγ H I detections isincluded in the Figure, but not used in the fit. The full-sample value of β = 1.65± 0.02 is shown as a blue diamond at the median absorberredshift along with the ±1σ range.
Fig. 14.— The observational Lyα decrement DA as a functionof redshift. The observed frame equivalent widths of all Lyα com-ponents within a particular redshift range are summed and di-vided by the clear pathlength in that range. Cosmic variancecontributes significant scatter to DA(z), but the data, binnedto ∆z = 0.05 (black data points), can be fitted with the formDA = 0.014 (1+z)2.2±0.2 (dashed line). The dotted line shows theHST/FOS fit of Kirkman et al. (2007); DA = 0.016 (1 + z)1.01.
the evolution of different metal-ion systems across a sig-nificant redshift range. We group metal systems intothe same six redshift bins used for H I and measure thecumulative dN (> N)/dz at the observational thresholdW ≥ 30 mA for metal species with both significant red-shift coverage and detection statistics. The results areshown in Figure 15 for O VI, N V, C III, and Si III. C IV
is sampled only over a small redshift range, and thereare not enough Si IV detections to provide any reliablestatistics when spread over multiple redshift bins. Wefollow the same procedure as used above for H I, calculat-ing integrated dN/dz profiles in up to five redshift bins.O VI systems at z < 0.1 and C III systems at z < 0.16are not observed in COS data, so comparable literature
Fig. 15.— Evolution of metal systems in four species. CumulativedN/dz as a function of redshift is shown for systems with rest-frame equivalent width Wλ > 30 mA. Lower limits are shown witharrows. Vertical dotted lines indicate the redshift range over whichthe species appears in COS data. OVI (upper left) and C III (lowerright) lack COS coverage at zOVI < 0.10 and zCIII < 0.16, soliterature values (blue, open symbols) from DS08 and referencestherein are plotted in place of the lowest-redshift bins. A dashedline shows the best fit to the OVI data; all other ions are consistentwith no evolution.
values from Danforth & Shull (2005) and Danforth et al.(2006) are used for the lowest-redshift data point.Statistics are poor even for O VI, the most numer-
ous of the metal-ion detections. O VI appears to evolvewith redshift in the same sense at H I. A fit of the formdN (> N)/dz ∝ (1 + z)γ gives γOVI = 1.8 ± 0.8 atz < 0.47 for Wλ > 30 mA including the z < 0.16 valuefrom Danforth & Shull (2005) at the lowest redshift bin.Evolution in the other three ions is poorly constrained.N V is fitted with γNV = 2.4 ± 2.9. Si III and C III,
HST/COS Survey of the Low-z IGM 25
Fig. 16.— Two-point correlation function in the low-z Lyα for-est. Observed component pairs (top) show significant signal at∆v ∼ 100 km s−1 compared with a sample of randomly-placedcomponents with the same overall dN/dz behavior (dashed line).The two-point correlation function, ξ(∆v) (bottom) shows thesame clustering at ∆v ∼ 100 km s−1, and no clustering at highervelocity separations. This is in contrast to the TPCF of galaxies togalaxies (dotted line; Penton, Stocke, & Shull 2004) which showedsignificant correlation at ∆v < 1000 km s−1. HST/STIS data fromTilton et al. (2012) show a similar behavior (open circles) includingthe smaller TPCF at low velocity separations.
which typically show properties correlated closely withH I (DS08), are also poorly constrained (γSiIII = −1± 2,γCIII = 1.7± 1.3). Much larger samples of metal-ion ab-sorbers are required before metal evolution can be mea-sured with any precision.
5.2. Clustering of IGM Absorbers
The two-point correlation function (TPCF) and its am-plitude ξ is often used as a measure of clustering in theuniverse (Peebles 1980). A value of ξ > 0 means thereis greater-than-random clustering, while ξ < 0 may indi-cate anti-clustering (voids). Since our sight lines samplephysically unrelated regions in almost all cases, we canuse absorbers along them to measure the velocity-spaceclustering of absorbing materials in the IGM.We calculate ξ(∆v) for Lyα components in each sight
line as
ξ(∆v) =Nobs(∆v)
Nran(∆v)− 1, (9)
where Nobs and Nran are the normalized number of ab-sorption component pairs with a given velocity sepa-ration per ∆v (Kerscher, Szapudi, & Szalay 2000) andwhere the velocity separation (z1 > z2) is defined rela-
Fig. 17.— Variation in TPCF with component strength. WeakH I absorbers (top, 12.5 < logN < 13.5) show only modest clus-tering, while stronger Lyα components (bottom, logN > 13.5)show significant clustering at ∆v ∼ 50 − 300 km s−1. The sametrend is apparent in the HST/STIS absorbers from Tilton et al.(2012) (open circles). Dotted lines show the galaxy-galaxy TPCFof Penton, Stocke, & Shull (2004).
tivistically
∆v
c≡
(1 + z1)2 − (1 + z2)
2
(1 + z1)2 + (1 + z2)2. (10)
The observed redshifts (z1, z2) are taken as any pairof Lyα components in the same sight line detected at≥ 4σ. For simplicity, and to avoid spurious clusteringsignal from velocity mis-matches between lines in differ-ent transitions, we use only Lyα components, not sys-tems in which different closely-spaced components maybe grouped together into the same system. In prin-ciple, any absorption component could be used. SeeLabatie et al. (2010) and references therein for a discus-sion of biases inherent to this and other estimators of theTPCF.The random absorber distribution is calculated with
a Monte-Carlo simulation using the detailed fit to theobserved IGM detection statistics and the actual datain each of the survey sight lines. Random componentlocations are simulated 100 times in each sight line. Asimilar technique was used by Penton, Shull, & Stocke(2000) and Penton, Stocke, & Shull (2004) to simulateabsorbers in HST/FOS and STIS data. At each resolu-tion element ∆v in each sight line, we calculate the 4σminimum equivalent width detection supported by theS/N of the data. The Wmin(λ) vector is converted toNLyα,min(z) and the probability of finding a component
26 Danforth et al.
Fig. 18.— Two-point correlation function of metal/non-metalabsorbers. IGM systems with absorption in at least one metalion (black filled circles) show a strong TPCF signal at ∆v ∼
100 km s−1, while H I-only systems (open squares) show littleor no signal. OVI λ1032 components (green diamonds) show asimilar TPCF behavior to metal-bearing H I systems.
in velocity resolution element dv is given from Eq. (7) as
Pran(z)=C0dv
c(1 + z)γ (11)
×
[
(
Nmin(z)
1014 cm−2
)−β
−
(
Nmax(z)
1014 cm−2
)−β]
.
Integrated over the Lyα pathlength in each component,Pran(z) should equal the number of observed Lyα sys-tems in that sight line, modulo cosmic variance. If theprobability at a given resolution element is greater thana randomly generated number, an absorption componentis placed at this redshift position. When the entire red-shift pathlength of the sight line has been processed, thenumber of pairs in the randomly-distributed componentsis found, and the result Nran(∆v) is added to a list ofrandom pairs. We produce strong and weak pairs by set-ting limits on Nmin and Nmax in both the observed andartificial component lists.Figure 16 shows the behavior of the observed and ran-
dom component pairs, normalized to the velocity widthin each bin in our sample (top panel). As expected,the distribution of random pairs is flat as a functionof ∆v. The observed pairs show a significant peak at50 . ∆v . 300 km s−1. The two-point correlationfunction (lower panel) shows a significant correlation at∆v . 300 km s−1 and no signal at much higher velocities.This is in contrast to the galaxy-galaxy TPCF (dotted)from Penton, Stocke, & Shull (2004) which shows signif-icant correlation at ∆v < 1000 km s−1 and significantanticorrelation at ∆v > 1000 km s−1. Whether this an-ticorrelation is due to the presence of voids in the galaxydistribution, or is an artifact of the TPCF methodology(Kaiser 1987) is unknown. We note that the random ab-sorber population (responsible for the denominator in ξ)is extremely sensitive to the fit parameters assumed forthe Lyα forest and its evolution. This introduces a smalluncertainty in the scaling of the ξ, but it does not changethe overall flat nature of Nran(∆v) seen in the top panelof Figure 16.Splitting the TPCF into strong and weak subsamples
(Figure 17), we see that the strong components (logN >13.5) show signal equal to or stronger than the galaxy-galaxy TPCF at ∆v ∼ 100 km s−1, while the weakercomponents (12.5 ≤ logN < 13.5) show a much smallerclustering signal. This is in keeping with the picture ofstrong H I systems in and around galaxy halos, which areclustered (Rudie et al. 2013), or at least associated withlarge-scale structures such as filaments. Neither strongnor weak samples show significant TPCF at 300 . ∆v .1000 km s−1 where the galaxies are still highly clustered(ξ & 3).The decrease in ξ at ∆v < 60 km s−1 shown in Fig-
ures 16 and 17 is intriguing. The velocity resolution ofCOS (∼ 17 km s−1) and the typical width of Lyα lines(b ∼ 33 km s−1, FWHM ∼ 55 km s−1) suggest thatthe low ξ values in the lowest-velocity bins may be dueto finite instrumental resolution, line blending, and re-lated systematic effects. Post-processing the Tilton et al.(2012) catalog of HST/STIS absorbers shows a similarturn-down at low velocities (open circles in Figures 16and 17). Since the resolution of the STIS/E140M grat-ing is ∼ 7 km s−1 (compared with ∼ 17 km s−1 forthe medium-resolution COS gratings), this hints that thedownturn at ∆v < 100 km s−1 may be a real effect.If real, this lack of correlation at the smallest velocitiesmay be indicative of the kinematics within galaxy halos.However, differentiating blended components, especiallyin strong absorbers, is subject to quite a bit of system-atic uncertainty and we view this apparent downturn inξ at the smallest ∆v as suggestive only. Unfortunately,resolution limitations restrict the ability of modern cos-mological simulations to track such small velocity sepa-rations (e.g., Cen & Chisari 2011).At smaller velocity separations, line blending intro-
duces uncertainties and biases into our ability to separateand identify velocity components. The median dopplerparameters of H I and O VI absorbers are comparable,at 〈b〉 ≈ 30 km s−1, and the full width at half maximum,∆vFWHM ≈ 1.67b is approximately 50 km s−1. We there-fore distrust any TPCF signal at those separations.Next, we investigate the clustering properties of IGM
systems with and without metal-ion absorption. We ap-proach this in two ways. First, we analyze IGM sys-tems rather than the individual Lyα components stud-ied above, so that metal absorption can more easily beassociated with H I columns despite small velocity un-certainties. Since systems have a minimum velocity half-width of 30 km s−1 and many systems (particularly metalsystems) are broader than this, we restrict this analysisto velocity separations ∆v > 60 km s−1. To generatea TPCF, we assume that the distribution of randomly-placed systems in both metal and non-metal systems isflat with ∆v, as in the top panel of Figure 16, and thatthere is no clustering at ∆v > 1000 km s−1. Secondly,in order to eliminate bias due to system definitions, weinvestigate the clustering of metal-ion components them-selves using the most commonly-seen transition (O VI
λ1032). This method is analogous to the Lyα TPCFshown in Figure 16. Again, we assume a flat distributionof randomly placed absorbers in each case.Figure 18 shows the clustering properties of the non-
metal systems (open circles) along with the metal sys-tems. Individual metal component clustering is shown
HST/COS Survey of the Low-z IGM 27
in the various colored symbols. The metal componentsample sizes are quite a bit smaller than the H I sampleor even the metal/non-metal system samples, and thusthe uncertainties are much larger. However, O VI λ1032components show a clustering signal at ∆v ≈ 100 km s−1
which is considerably stronger than that seen in themetal systems. This difference may be a result of our pro-cess of system definition: in many cases, closely-spacedmetal components are grouped together into a single sys-tem which will systematically reduce the number of ab-sorber pairs at close velocity separation.Qualitatively, metal-ion components and metal-
bearing systems show strong clustering (ξ ∼ 10 − 50)peaked at ∆v ∼ 50 − 200 km s−1, albeit with substan-tial uncertainty due to the small size of the samples.Pieri et al. (2014) see a similar trend in a large sampleof Lyα absorbers at 2.4 < z < 3.1 in the BOSS sur-vey, with a high degree of clustering and correlation ofmetal absorbers at scales down to ∆v ≈ 130 km s−1 (theresolution limit of their data). The non-metal systemsshow a peak ξ . 1 at the same velocity range. This sug-gests that most of the radial-velocity clustering in theIGM can be attributed to strong, metal-bearing systemsin the CGM, again consistent with the picture of strong,metal-enriched absorption being associated with galaxyhalos.
6. SUMMARY OF PRIMARY RESULTS
We present a high-quality, medium-resolutionHST/COS survey of the IGM along 82 UV-bright AGNsight lines. Because the sight lines were chosen forsensitivity to weak IGM absorbers at low-redshift overthe maximum pathlength, we favor targets observedwith both the COS/G130M and G160M gratings witha typical S/N & 15 per resolution element. We limitthe redshifts of the AGN to 0.05 < zAGN < 0.85 tomaximize IGM pathlength for species of interest (H I,O VI, etc.) while minimizing line confusion.The spectra were processed with semi-automated con-
tinuum fitting and line-finding/measurement routines tominimize the subjective bias associated with many pre-vious IGM surveys. The identity of absorption featuresis established through a manual process and lines areremeasured as necessary. Galactic, instrumental, andprobable AGN-intrinsic features are flagged. In total,5138 individual lines are identified as absorption fromintervening material in the IGM. This includes 4234 Lyαlines, 606 Lyβ lines, and 1633 metal-ion lines represent-ing 25 metal ion species. The median and ±1σ redshiftof absorption systems is z = 0.14+0.18
−0.10.To better facilitate comparisons between species at the
same redshift, the IGM lines are grouped by into 2611distinct redshift systems of which 418 are detected in atleast one metal line. The most common metal species isO VI (present in 280 systems) followed by C III (115),Si III (123), C IV (70), and N V (59). Ne VIII is onlydetected at a significant level in three systems.We present below a summary of our primary science
results:
• The fraction of IGM H I systems detected in one ormore metal ions is a strong function of NHI. Met-als are rarely (less than 10%) detected in NHI .1013.5 cm−2 absorbers, but become nearly ubiqui-
tous for strong systems (NHI & 1015 cm−2).
• The cumulative distribution of H I absorbers at z ≤0.47 in the sample (Figure 5) follows a power lawin H I column density of the form dN (> N)/dz =C14 (N/1014 cm−2)−(β−1) over the column densityrange 12 ≤ logNHI ≤ 17 with normalization C14 =25± 1 and differential index β = 1.65± 0.02.
• Dividing the sample into redshift bins of ∆z ≈ 0.1and analyzing the subsamples, we see clear evolu-tion in both the slope β and normalization C14 ofthe distribution. We parameterize the evolution ofthe diffuse IGM H I absorbers as dN (> N)/dz =C0 (1+ z)γ (N/1014 cm−2)−[β(z)−1] with C0 = 16±1, γ = 2.3, and β(z) = (1.75±0.03)−(0.31±0.10) zfor z ≤ 0.47.
• Metal systems analyzed in the same manner as H I
suggest that O VI evolves in the same sense as H I
with γ ∼ 1.8± 0.8. Smaller samples of N V, C III,and Si III absorbers do not present clear evidencefor evolution.
• We calculate the contribution to the closure densityby a particular species, Ωion, and the contributionrepresented by gas which is traced by a particular
species, Ω(ion)IGM . The values given in Table 6 are
consistent with previous surveys.
• A two-point correlation function (TPCF) of Lyαcomponents shows that there is significant clus-tering of IGM absorbers in radial velocity. Fig-ure 16 shows a significant clustering signal at∆v = 60 − 300 km s−1 and little or no signalat higher velocities. This is in contrast to thegalaxy-galaxy radial velocity clustering found byPenton, Stocke, & Shull (2004) which shows sig-nificant clustering at ∆v < 1000 km s−1. Di-viding the sample into strong (logNHI > 13.5)and weak (logNHI < 13.5) absorbers, we see thatnearly all of the clustering signal is accounted for bythe stronger systems. Examining metal and non-metal systems reveals an extremely strong TPCFfor metal systems as well as in components of com-mon metal-ion transitions (O VI 1032 A, etc.)
This paper represents many years of work by the COSScience Team to characterize the baryon content, struc-ture, and metallicity of the low-redshift IGM. At thispoint, it is worth summarizing the status of low-redshiftIGM surveys and models: What properties of the IGMare now well established? What are the remaining uncer-tainties? Where are avenues for future observations andtheoretical work? The COS (G130M/G160M) spectrahad resolving power R ≈ 18, 000 and identified ∼ 2600IGM absorbing system and 418 metal-line systems (280O VI, 70 C IV). The total redshift path length, ∆z =21.7, is four times larger than our previous low-redshiftsurveys with STIS (Danforth & Shull 2008; Tilton et al.2012) which identified 650-750 Lyα absorbers. For com-parison, the Quasar Absorption Line Survey HST/FOSKey Project (Jannuzi et al. 1998) observed 83 AGN sightlines. Although the FOS survey covered a larger pathlength, ∆z ≈ 49, its line sample was smaller, with 1129
28 Danforth et al.
Lyα lines, 107 C IV systems, and 41 O VI systems, andthe spectra were obtained at an order-of-magnitude lowerresolution (R ≈ 1300).With the larger COS medium-resolution IGM survey,
the bivariate distribution of H I absorbers, f(NHI, z), isnow well established at redshifts z ≤ 0.4. Its parameter-ization in column density and redshift are fitted to the
formN−βHI (1+z)γ, with a differential low-redshift slope of
β = 1.65±0.02 over the range 12 < logNHI < 17, identi-cal to the slope at 〈z〉 = 2.4 found by the Keck BaryonicStructure Survey for Lyα absorbers with logNHI > 13.5.(An earlier survey with VLT/UVES by Kim et al. (2002)found β ≈ 1.5 over the range 1.5 < z < 4.0.) TheCOS survey has also established that strong H I ab-sorbers evolve faster than weak absorbers, with redshift-evolution indices γweak = 1.15± 0.05 for 13 < logNHI <14 and γstrong = 2.3 ± 0.1 for logNHI > 14. How-ever, for the low column density absorbers, the dN/dzvalues determined in this work are somewhat too highto join seamlessly with the higher redshift line densitiesfound from ground-based spectroscopy using the slopesobtained by both the COS and ground-based studies. Forthe higher column density absorbers, a slope change is re-quired somewhere between 0.4 . z . 2. The evolution ofthe IGM cannot be well-understood at all cosmic epochssimply by interpolating between the the low (z . 0.4)and high redshift (z & 2) regimes.For the cosmological baryon census, the COS sur-
vey confirms previous studies (Shull, Smith, & Danforth2012; Tilton et al. 2012) which found that ∼ 20−25% ofthe baryons reside in the intergalactic Lyα forest, rang-ing in column density from 12.8 < logNHI < 16.0. Ad-ditional matter exists in higher column density systems,extending up to logNHI ≈ 19 (Penton, Stocke, & Shull2004). With nearly 2600 H I absorbers, the COS surveyprovides better statistics. Some uncertainties remain forweak Lyα lines with equivalent widths Wλ < 30 mA(logNHI < 12.74) and for rare high-column density sys-tems. For strong absorbers (logNHI > 15) our surveytypically has fewer than 10 Lyα lines per column den-sity bin (∆ logN = 0.2). In addition, the ionizationcorrections required to convert the absorber distribu-tion, f(NHI, z), into a baryon density parameter, Ωb,involve uncertain physical parameters such as the ion-izing UV background and hydrogen photoionization rate
(ΓH), characteristic absorbers sizes, and cloud temper-ature. In fact, the column density distribution of Lyαabsorbers can be used, together with cosmological sim-ulations, to constrain the amplitude of the low-redshftionizing background (Kollmeier et al. 2014; Shull et al.2015).Further progress in characterizing the evolution of
structure in the low-redshift IGM will likely occur alongseveral fronts. First, we need to characterize the H I
distribution at the low-column end, searching for the ex-pected turnover in the distribution below logNHI < 12.4.This will require high-S/N data to measure Lyα equiv-alent widths down to 5 mA (logNHI ≈ 12.0.) Second,we have little knowledge of the evolution of the IGMbetween z ≈ 1.5 and z ≈ 0.4, the epoch when physicalconditions change rapidly as star-formation rates decline.Probing the IGM beyond z > 0.4 will require extensivesurveys in the “near-ultraviolet desert” (λ > 1700 A),both for Lyα absorbers as well as key lines of carbon,oxygen, nitrogen, and silicon that measure the extent ofmetal transport from galaxies into the IGM.
It is our pleasure to acknowledge fruitful discussionswith many colleagues during the course of this workincluding Ben Oppenheimer, Devin Silvia, and JoshuaMoloney. Julie Davis (Colorado), Alex Filippenko, BradCenko, Weidong Li, Weikang Zhang (U. C. Berkeley),Meg Urry, Erin Bonning Wells, and Jedidah Isler (Yale)were instrumental in the acquisition of several of thedatasets used in this study. We also acknowledge thevaluable contributions made by our anonymous refereewho provided the thorough, critical, expert review andlead to a much-improved scientific paper. This workwas supported by NASA grants NNX08AC14G (COSScience Team), HST-AR-1243.06, HST-GO-12612.01-A,and HST-GO-13008.01-A to the University of Coloradoat Boulder. BAK and JTS acknowledge support fromNSF grant AST1109117. JMS acknowledges NSF grantAST07-07474 and thanks the Institute for Astronomy atCambridge University for support as a Sackler VisitingLecturer. Scott Fleming at MAST was instrumental inthe production of the High-Level Science Products asso-ciated with this survey. The authors made extensive useof the MAST, NED, and ADS Archives during this work.
Facility: HST (COS), FUSE, HST (STIS)
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