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DEEP SURVEYS: Galaxy formation and evolution

Olivier Le Fèvre , Laboratoire d’Astrophysique de Marseille. DEEP SURVEYS: Galaxy formation and evolution. INTRODUCTION. What is “Observational Cosmology” ?.

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DEEP SURVEYS: Galaxy formation and evolution

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  1. Olivier Le Fèvre, Laboratoire d’Astrophysique de Marseille DEEP SURVEYS:Galaxy formation and evolution

  2. INTRODUCTION

  3. What is “Observational Cosmology” ? Observational Cosmologyis the study of the structure, the evolution and the origin of the universe through observation using instruments such as telescopes Accuratefacts, measurements and theirerrors No place for speculation !

  4. What 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 surveys • Deep spectroscopic redshift surveys Surveys rely on large number statistics

  5. Surveys = polls • Ask the opinion of 1 person: alwayswrong • Ask 10 persons: strongbiases • Ask 100 persons: somebiases • Ask 1000 persons: averageisprobably close to truth • … • Votes from the whole population make the truth

  6. Ban the bad habits !! • Astrophysics has a bad habit: generalize from a single observation • The goal is that you’ll leave these lectures with a critical eye on observations presented in the literature

  7. Plan of these lectures • Surveys: observables • Surveys: methods and observations • The Universe on large scales • The mass assembly and global star formation history • The most distant galaxies • Future Surveys

  8. LECTURE #1

  9. Whymeasure the Universe ? • Science knowseverything ! • We know the cosmological model ! So whybother ??

  10. Cosmologyisconstantlyevolving… Dogons Greeks Copernicus Modern: Big Bang Tomorrow ?

  11. Cosmological model • Based on General Relativity • A theoretical description • Validated by some key observables • Expansion of the universe • Temperature of the microwave background • Cosmicabundance of elements

  12. “Accurate” cosmological parameters show our ignorance ! DarkMatter: 26.8% OrdinaryMatter: 4.9% DarkEnergy: 68.3% WhatisDarkMatter ? WhatisDarkEnergy ? Need more observations !

  13. Models and Simulations • Standard CDM in a computer • Darkmatter simulations • Addphysical prescriptions on top of DM • Semi-analyticalmodels • Hydro simulations • …

  14. MILLENIUM II simulation Simulations produce FAKE universes ! Modelsneed to implementeverincreasingcomplexity Models are veryuseful to understand main physicalprocesses and interplay Cosmic Time

  15. Today BigBang Cosmic Time Differentmodels = Differentappearance of the universeatdifferentcosmic times Differentmodels NEED Observations !

  16. Tracingevolution • Comparing the properties of galaxies atdifferentepochsalongcosmic time allows to deriveevolution • Caveat: wecannotfollow the same galaxies, hencewe have to inferwhois the progenitor of whom

  17. What do wewant to measure ? Atdifferentredshifts: evolution

  18. Deepgalaxysurveys Distribution in LSS Luminosity / SFR / Mass evolution Specifc populations N(z) Oldest Galaxies Luminosity Function Galaxy density field Stronglystarforming gal. Luminosity Density Correlation function QSO / AGN SFR Cosmologicalparameters Clusters / groups Mass function Track evolution versus Environment, Luminosity, galaxy Type,… Need Observations !

  19. The main tracer of the universe: Galaxies • Galaxies are (biased) tracers of the darkmatter distribution • The biascanbemodeled (?) • Observe galaxies and you’ll know (almost) all about the universe • Formation and evolution of galaxies • Darkmatter content in galaxies and clusters • Cosmologyfromtheir distribution

  20. Observables in deepsurveys: I. Direct measurements • Positions in space: 3D + time • Apparent magnitudes and flux • Sizes, morphology

  21. Direct surveymeasurements: I.a. Positions in space • Measure the positions on the plane of the sky • Deep images • Measure the distances, using the redshift and a cosmological model • Redshift measurement • Redshift space vs. Real space Seealso the ‘cosmological distance ladder’

  22. Photometryfromdeep images SExtractor Does all whatyouneed: astrometry, magnitudes, basic shapes See: Bertin and Arnouts, 1996, A&AS, 117, 393 and ‘SExtractor for dummies’ (Beware of the ‘black box’ syndrom)

  23. Measuring image positions/astrometry • Use first moments of light distribution • Deblending crucial, the fainter the objects are

  24. The Redshift / • The shift in observed vs. emittedwavelengthis a consequence of motion • Blueshiftwhenmovingtowards the observer • Redshift whenmovingawayfrom the observer • In an expandingUniverseobjects are movingawayfromeachother: Redshift • Redshift is distance: v=Hd • Looking to a galaxy in rotation: velocityfieldwithblue/red shift

  25. Measuringphotometricredshifts • Photo-z is a redshiftderivedfromphotometric data • Use the SED (Spectral Energy Distribution) • Correlateagainst a set of templates • Sameprocessgives *-mass, SFR, age, etc. • Accuracyz~3-5% • Probability distribution function • Pb of catastrophicredshifts

  26. Measurespectroscopicredshifts Identifyobserved spectral features to rest-frame knownfeatures • Identifyemission / absorption features • Take continuum intoaccount Cross-correlation to galaxytemplates (Tonry & Davis, 1979, AJ, 84, 1511)

  27. Rest-frame spectrum EZ engine: Garilli et al., 2010, PASP, 122, 827

  28. Comparing photo-z and spec-z Photo-z Spec-z • Accuracy dz~0.05(1+z) • Trained on Spec-z • Catastrophicfailures: a few % • All objectsdetected in photometry • 1 magnitude deeperthanspec-z • Accuracy dz~0.001 • Accurate 3D mapping • Incompletness ~10-15% • Evaluatedwith photo-z • 30-70% of the objectsseen in photometry Complementary !

  29. Excellent photo-z, calibrated on spec-z, Ilbert+ 13

  30. Distances and Peculiarvelocities • Galaxies have a velocity component separatefrom the Hubble flow vpec=vobs-H0d • Particularly visible in clusters because of high velocity dispersion • Finger of Godeffect • Distances derivedfromredshiftmeasurementsneed to becorrected for this

  31. I.b. Apparent magnitudes and flux • Once objects are identified, get the total observed flux on an image • Sum the number of photons on detector • Calibratedusingreference sources • Apparent magnitude m=-2.5log(Flux)+C • SExtractor • In a spectrum, get the flux in a spectral line • Sum all the photons in a line • Computeequivalentwidth

  32. I.c. Sizes • Apparent sizes in arcsec, arcmin, deg • Galaxies z>0.5: arcsec-scale • Clusters of galaxies z>0.5: arcmin-scale 5” 65”=1.08’

  33. I.c. Morphology • Morphology of extra-galacticobjects • Galaxies • Clusters/groups • Galaxies • Parametric • Non-parametric

  34. Parametric fit to morphology • Represent a galaxy by a 2D model • Typical profile for spirals (exponential disc), and ellipticals (r1/4 profile) • GeneralizedSersic profile • Offers a basis to automated classification • Becomescomplicatedat z>1 • Galaxies becomemostlyirregular See CAS (Concentration-Asymetry-Clumpiness) non-parametric classification

  35. Observables in deepsurveys: II. Indirect measurements • Relative velocities, velocityfields, local density • Physical sizes • Absoluteluminositiesand flux • Stellar masses, star formation rate, age, metallicity, dust,… • Look-back time

  36. II.a. Relative velocities, velocityfields • In galaxies • Rotation, sub-components • Between galaxies • Mergers, dv<500km/s • In clusters • Velocity dispersion gives the cluster mass if virialized

  37. II.a. Local density • Densityexcess over mean • Environment- dependentproperties

  38. II.b. Physical sizes • Transformobservedangular size to physical dimension at the source : via the cosmological model • Use angulardiameter distance • Seecosmologycalculators: http://www.bo.astro.it/~cappi/cosmotools • Examples: @z=1 1deg=29Mpc @z=5 1deg=23Mpc For CDM Angular scale kpc/” Redshift

  39. II.c. Absoluteluminosities • Transform apparent to absolute magnitude Absolute in band Q Apparent in band R Distance modulus K correction

  40. II.d. Stellar mass, star formation rate, age, dust,… • Stellar populations add up to produce a galaxyluminosity and colors • Stellar population synthesismodelsaimatreproducing the observedstellar light from galaxies • SeeBruzual and Charlot, 2003, MNRAS, 344, 1000 • Includes changes withage, withmetallicity • Extinction lawfromdust • Difficultieswithdegeneracy • Age vs. Metallicity • IMF and SFR laws Syntheticspectra vs. Age (atfixedmetallicity)

  41. II.d. Spectral energy distribution fit by models Photometry: over a broadwavelength range: - Tracer of stellar populations - Measurement of *-mass (red SED) - Measurement of star formation (blue SED) - Extinction - Age (of last burst of SF) Photometricmeasurements SED fit withstellar population template

  42. II.e Look-back time • The redshift – distance relation isalso a distance-cosmic time relation • Look-back time: the time ittakes the light to come from an object at redshift z

  43. Observables in deepsurveys: III. Statisticalmeasurements • Counts N(m), N(z) • Luminosity Functions, Luminosity Density and Star Formation Rate • Mass Functions, Mass Density • Correlation functions, HOD

  44. III.aCounts N(m) • Count galaxies as a function of magnitude • Depends on the band/wavelength • History: “blue galaxy counts excess”

  45. III.bLuminosityfunction • Luminosity Function: counts of galaxies per luminosity, per unit volume • Parametrized as a Schechter function • * = characteristic density • L*= characteristic luminosity • = faint end slope

  46. III.bLuminositydensity, SFRD • Luminosity Density: mean luminosity per unit volume • Integrate LF • SFRD: use prescriptions to transform flux into star formation • UV • IR • H • …

  47. III.c Mass Function and density • Mass Function: counts of galaxies per stellar mass, per unit volume • Stellar mass density: integrate Mass Function

  48. III.dCorrelationFunction • Excessprobability over randomthat a galaxy in dV2 willbefoundat a distance r12from a galaxy in dV1 • Containscosmological information • Small scales: redshiftspacedistortions • Large scales: Baryon acoustic oscillations • Halo occupation • Power spectrum P(k): Fourier Transform of Correlationfunction • In practice (G: galaxysample, R: randomsample): • Angular CF: w() • 2D: (rp,) • Projected:  rp

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