Observational cosmology

DEEP SURVEYS:Galaxy formation and evolutionOlivier Le Fvre, Laboratoire dAstrophysique de Marseille1INTRODUCTION2What is Observational Cosmology ?Observational Cosmology is the study of the structure, the evolution and the origin of the universe through observation using instruments such as telescopes

Accurate facts, measurements and their errors

No place for speculation !

3What are deep surveys ?Deep galaxy surveys are observations of a part of the sky, assembling representative samples of galaxies from well defined selection criteria

Two types of complementary surveys:Deep photometric surveysDeep spectroscopic redshift surveys

Surveys rely on large number statistics4Surveys = pollsAsk the opinion of 1 person: always wrongAsk 10 persons: strong biasesAsk 100 persons: some biasesAsk 1000 persons: average is probably close to truthVotes from the whole population make the truth5Ban the bad habits !!Astrophysics has a bad habit: generalize from a single observationThe goal is that youll leave these lectures with a critical eye on observations presented in the literature6Plan of these lecturesSurveys: observablesSurveys: methods and observationsThe Universe on large scalesThe mass assembly and global star formation historyThe most distant galaxiesFuture Surveys7LECTURE #18Why measure the Universe ?Science knows everything ! We know the cosmological model !

So why bother ??9Cosmology is constantly evolving

GreeksDogonsCopernicus

Modern: Big Bang10?

TomorrowCosmological modelBased on General RelativityA theoretical descriptionValidated by some key observablesExpansion of the universeTemperature of the microwave backgroundCosmic abundance of elements 11

12Accurate cosmological parameters show our ignorance !Dark Energy: 68.3%Dark Matter:26.8%Ordinary Matter:4.9%What is Dark Matter ?What is Dark Energy ?Need more observations !13

Models and SimulationsStandard CDM in a computerDark matter simulationsAdd physical prescriptions on top of DMSemi-analytical modelsHydro simulations14Simulations produce FAKE universes !

Models need to implement ever increasing complexity

Models are very useful to understand main physical processes and interplay

MILLENIUM II simulationCosmic Time15

Different models=Different appearance of the universe at different cosmic timesCosmic TimeTodayBigBangDifferent modelsNEED Observations !16Tracing evolutionComparing the properties of galaxies at different epochs along cosmic time allows to derive evolutionCaveat: we cannot follow the same galaxies, hence we have to infer who is the progenitor of whom17What do we want to measure ?At different redshifts:evolution1819Deep galaxy surveysDistribution in LSSN(z)Galaxy density fieldSpecifc populationsOldest GalaxiesQSO / AGNStrongly starforming gal.Luminosity / SFR / Mass evolutionLuminosity FunctionTrack evolution versus Environment, Luminosity, galaxy Type,Correlation functionLuminosity DensitySFRMass functionClusters / groupsNeed Observations !Cosmological parametersThe main tracer of the universe: GalaxiesGalaxies are (biased) tracers of the dark matter distributionThe bias can be modeled (?)Observe galaxies and youll know (almost) all about the universeFormation and evolution of galaxiesDark matter content in galaxies and clustersCosmology from their distribution 20Observables in deep surveys: I. Direct measurementsPositions in space: 3D + timeApparent magnitudes and fluxSizes, morphology

21Direct survey measurements: I.a. Positions in spaceMeasure the positions on the plane of the skyDeep imagesMeasure the distances, using the redshift and a cosmological modelRedshift measurement Redshift space vs. Real spaceSee also the cosmological distance ladder22Photometry from deep imagesSExtractor

Does all what you need: astrometry, magnitudes, basic shapes

See: Bertin and Arnouts, 1996, A&AS, 117, 393andSExtractor for dummies

(Beware of the black box syndrom)

23Measuring image positions/astrometryUse first moments of light distribution

Deblending crucial, the fainter the objects are

24The RedshiftThe shift in observed vs. emitted wavelength is a consequence of motionBlueshift when moving towards the observerRedshift when moving away from the observerIn an expanding Universe objects are moving away from each other: RedshiftRedshift is distance: v=HdLooking to a galaxy in rotation: velocity field with blue/red shift25

Measuring photometric redshiftsPhoto-z is a redshift derived from photometric dataUse the SED (Spectral Energy Distribution)Correlate against a set of templatesSame process gives *-mass, SFR, age, etc.Accuracy z~3-5%Probability distribution functionPb of catastrophic redshifts

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Measure spectroscopic redshiftsIdentify observed spectral features to rest-frame known featuresIdentify emission / absorption featuresTake continuum into account

Cross-correlation to galaxy templates (Tonry & Davis, 1979, AJ, 84, 1511)

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Rest-frame spectrum

EZ engine: Garilli et al., 2010, PASP, 122, 82728Comparing photo-z and spec-zPhoto-zSpec-zAccuracy dz~0.05(1+z)Trained on Spec-zCatastrophic failures: a few %All objects detected in photometry1 magnitude deeper than spec-z

Accuracy dz~0.001Accurate 3D mappingIncompletness ~10-15%Evaluated with photo-z30-70% of the objects seen in photometry

Complementary !29Excellent photo-z, calibrated on spec-z, Ilbert+ 1330

Distances and Peculiar velocitiesGalaxies have a velocity component separate from the Hubble flowvpec=vobs-H0dParticularly visible in clusters because of high velocity dispersionFinger of God effectDistances derived from redshift measurements need to be corrected for this

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32I.b. Apparent magnitudes and fluxOnce objects are identified, get the total observed flux on an imageSum the number of photons on detectorCalibrated using reference sourcesApparent magnitude m=-2.5log(Flux)+CSExtractorIn a spectrum, get the flux in a spectral lineSum all the photons in a lineCompute equivalent width

33I.c. SizesApparent sizes in arcsec, arcmin, degGalaxies z>0.5: arcsec-scaleClusters of galaxies z>0.5: arcmin-scale

65=1.08

534I.c. MorphologyMorphology of extra-galactic objectsGalaxiesClusters/groupsGalaxiesParametricNon-parametric

35Parametric fit to morphology

See CAS (Concentration-Asymetry-Clumpiness) non-parametric classification36Observables in deep surveys: II. Indirect measurementsRelative velocities, velocity fields, local densityPhysical sizesAbsolute luminosities and fluxStellar masses, star formation rate, age, metallicity, dust,Look-back time

37II.a. Relative velocities, velocity fields

38II.a. Local densityDensity excess over mean

Environment- dependent properties

39II.b. Physical sizesTransform observed angular size to physical dimension at the source : via the cosmological modelUse angular diameter distance

See cosmology calculators:http://www.bo.astro.it/~cappi/cosmotoolsExamples: @z=1 1deg=29Mpc@z=5 1deg=23Mpc

Angular scale kpc/RedshiftFor CDM40II.c. Absolute luminositiesTransform apparent to absolute magnitude

Apparent in band RAbsolute in band QDistance modulusK correction41II.d. Stellar mass, star formation rate, age, dust,Stellar populations add up to produce a galaxy luminosity and colorsStellar population synthesis models aim at reproducing the observed stellar light from galaxiesSee Bruzual and Charlot, 2003, MNRAS, 344, 1000Includes changes with age, with metallicityExtinction law from dustDifficulties with degeneracyAge vs. MetallicityIMF and SFR laws

Synthetic spectra vs. Age(at fixed metallicity)42

II.d. Spectral energy distribution fit by models43Photometry: over a broad wavelength range:- Tracer of stellar populations- Measurement of *-mass (red SED)- Measurement of star formation (blue SED)- Extinction- Age (of last burst of SF)Photometric measurementsSED fit with stellar populationtemplate II.e Look-back timeThe redshift distance relation is also a distance-cosmic time relationLook-back time: the time it takes the light to come from an object at redshift z44

Observables in deep surveys: III. Statistical measurementsCounts N(m), N(z)Luminosity Functions, Luminosity Density and Star Formation RateMass Functions, Mass DensityCorrelation functions, HOD

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46III.a Counts N(m)Count galaxies as a function of magnitudeDepends on the band/wavelength

History: blue galaxy counts excessIII.b Luminosity functionLuminosity Function: counts of galaxies per luminosity, per unit volumeParametrized as a Schechter function* = characteristic densityL*= characteristic luminosity= faint end slope

47III.b Luminosity density, SFRDLuminosity Density: mean luminosity per unit volumeIntegrate LF

SFRD: use prescriptions to transform flux into star formationUVIRH

48III.c Mass Function and densityMass Function: counts of galaxies per stellar mass, per unit volumeStellar mass density: integrate Mass Function

49III.d Correlation FunctionExcess probability over random that a galaxy in dV2 will be found at a distance r12 from a galaxy in dV1

Contains cosmological informationSmall scales: redshift space distortionsLarge scales: Baryon acoustic oscillationsHalo occupationPower spectrum P(k): Fourier Transform of Correlation functionIn practice (G: galaxy sample, R: random sample):Angular CF: w()2D: (rp,)Projected:

rp

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From Sylvain de la Torre51III.d Correlation Function: Redshift Space DistortionsDeviations from Hubble flow produce flattening of CF on large scales along line of sight This is linked to the growth rate of structures

52III.d Correlation Function: BAOBaryon acoustic oscillations produced when photons decoupled from matter at recombinationLeaves a signature in the CFPeak at ~100 h-1 Mpc

53Observables in deep surveys: IV. Cosmological parametersHubble constant, age of the universeDensity parametersmbEquation of state

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