1. Probing the formation and evolution of massive galaxies with near- to mid-infrared surveys Scuola Nazionale di Astrofisica VIII Ciclo (2005-2006): I Corso S. Agata sui due golfi, 8-13 Maggio 2005 LECTURE 1 Andrea Cimatti (INAF-Arcetri)

2. Lecture 1 - Outline Cosmological framework Hierarchical galaxy formation Main properties of z~0 galaxies Methods and tools used to interpret survey results (1) Spectral synthesis models (2) SED fitting and stellar masses (3) Quantitative morphology (4) Clustering analysis How to define and plan a galaxy survey: ingredients and problems Just a brief introduction

3. The cosmological framework Friedmann-Lemaitre-Robertson-Walker model Friedmann equation H = expansion rate a(t) = cosmic scale factor = 1+z ρ tot = mass density R curv = curvature radius, R curv,0 = (1/H 0 )/(Ω 0 -1) 1/2 k = 0 (negative, flat, positive curvature) Ω 0 = ρ tot /ρ crit = ρ tot /(3H 0 2 /8πG) w X = characterizes the pressure of dark energy = p X /ρ X 10 parameters describe expansion, geometry, age and composition of the Universe Deceleration parameter Age of the Universe

4. “Precision” cosmology

5. Cosmological parameters

6. z=1: 7.7 Gyr ago z=3: 11.5 Gyr ago z=6: 12.7 Gyr ago z=10: 13.2 Gyr ago z=∞: 13.7 Gyr ago (age of the Universe) H 0 =70 km/s/Mpc Ω m =0.3 Ω Λ =0.7

7. H Hierarchical galaxy formation

8. Time z=10 z=5 z=2 z=0 (now) redshift Structure formation with CDM

9. Merging trees Baugh et al 98

10. The complex problem of baryonic matter evolution Cole et al. 2000

11. Constraints and predictions

12. Example: hierarchical growth of galaxy masses Baugh et al. 2002

13. H Spheroids

14. Baldry et al. 2004 (SDSS) Up to 75% of the total stellar mass at z=0 is in spheroids (E/S0) Characteristic stellar mass ~ 10 11 Msun Strongly clustered: tracers of large scale structure evolution Linked to massive black hole evolution via the M(bulge)-M(BH) relation The role of spheroids Also known as early-type or E/S0 galaxies

15. Example Hierarchical formation of a massive elliptical galaxy N-body numerical simulation (Meza et al. 03) Possible mergers: Disk-disk Disk-spheroid Spheroid-Spheroid

16. Example: formation of massive spheroids

17. Present-day massive elliptical galaxy “Monolithic collapse” (unrealistic in CDM bottom-up framework): High-z, rapid formation, strong SF episode followed by passive evolution (or “pure luminosity evolution”, PLE) (Eggen & Lynden-Bell 1962, Larson 1974, Searle & Zinn 1977) “Fast” merging/starburst mimicking the old-fashioned monolithic collapse High-z, strong SFR, stop SFR ?, followed by passive evolution + minor merger events ? Alternative scenarios

18. H Galaxies at z=0

19. The Hubble classification

20. Normal galaxy spectra: UV-optical Star-forming Weakly star-forming Passive [OII]3727 Hβ [OIII]4959+5007 HαHα CaII H&K D4000 Starburst Irregular Late spirals Early spirals Ellipticals Lenticulars

21. Normal star-forming galaxy spectra: mid-IR Spitzer spectrum of a starburst galaxy (NGC 7714) (Brandl et al. 2004) PAH = Policyclic Aromatic Hydrocarbons The spectra of spheroidal galaxies are dominated by old stars -> they fade in the mid-IR Star-forming galaxies: strong mid-IR emission with complex spectra

22. Normal star-forming galaxy spectra: far-IR Blain et al. 2002

23. Stellar mass fractions Fraction of the total stellar mass in the local Universe: higher in massive galaxies with high surface mass density, strong D(4000) breaks and red colors Based on the Sloan Digital Sky Survey (SDSS) sample of 10 5 galaxies at low redshift Cyan = low density environment, Red) = high density environment (Kauffmann et al. 2003)

24. Bimodality of galaxies M(crit) = 3 x 10 10 Msun Galaxies are divided into two main families (bimodality): M<M(crit): blue, less luminous, weak D(4000), Hδ emission M>M(crit): red, more luminous, strong D(4000), Hδ absorption (Kauffmann et al. 2003) r

25. Galaxy type – density relation Modern version of the “morphology – density” relation (Dressler 1980) The denser the environment, the larger the number of elliptical galaxies Kauffmann et al. 2003

26. Dynamical mass vs. near-IR luminosity logM(dyn)=log[L(H)]+0.66 M(dyn)/L(H) = const = 4.6 At shorter wavelengths (UBV) the M/L ratio increases with mass  The near-IR luminosity is an accurate tracer of the mass (Gavazzi et al. 1996)

27. H Tools (I) Spectral synthesis and analysis

28. Spectral synthesis models: stars Isochrones IMF and range of stellar masses Theoretical and/or empirical stellar spectra to get out magnitudes, colors, SEDs from each point of the isochrones Star formation histories (SFHs) Range or grid of metallicities Bruzual & Charlot 2003 Age (Gyr) Ψ(t) = star formation rate ζ(t) = metal-enrichment law t ’ = age of SSP = power radiated per unit initial mass by a SSP of age t ’ and metallicity ζ(t-t ’ ) at age t ’ SP with any SFH can be expanded in series of instantaneous bursts (SSPs). The SED of a stellar population at time t is: (e.g. Tinsley 1980)

29. The contribution of different stars Spectral synthesis models allow us to derive the relative contributions of different stellar populations

30. Spectral synthesis models: gas & dust New models include the self-consistent treatment of: Gas Dust extinction Dust thermal emission Metallicity evolution (e.g. GRASIL, Granato & Silva; STARDUST, Devriendt et al.)

31. M/L(K) ratio vs. age and (B-V) color Solid= Salpeter IMF, Dotted: Scalo IMF From top to bottom: τ=1, 5, 10, 20 Gyr (Madau et al. 1998) Stellar mass/light ratios Dependence of M(stars)/L on photometric band, age and initial mass function (IMF) IMF: f(m) = A m -x Probability of a star forming with a particular mass

32. Example using D(4000) and Hδ as tracers of galaxy star formation histories (Kauffmann et al. 03 )

33. Redshift Reddening gas line ratios Stellar content and age Ionized gas metallicity SFR from em.lines and UV Kinematics (high-res) Dynamical masses Scaling relations (T-F, FP) Ionization diagnostics Emission line FWHM (AGN) Clustering Spectral analysis: derivable information

34. H Tools (II) Spectral Energy Distribution (SED) fitting

35. Multi-band photometry (SED fitting) M/L ratio (from spectral synthetis models) Stellar masses Caveat: IMF, age-dust – metallicity degeneracy

36. Photometric stellar masses Dynamical masses very difficult or impossible to estimate at high redshifts Dynamical masses possible only for very small samples Drory et al. 2004

37. Are photometric stellar masses reliable ? di Serego Alighieri et al. 2005 Drory et al. 2004 Brinchmann & Ellis 2000

38. H Tools (III) Quantitative morphological analysis

39. Morphology Sersic law: I(r) = I e exp {-b[(r/r e ) 1/n -1]} I(r)= surface brightness (flux/arcsec 2 ) I e = surface brightness within r e r e = radius containing 50% of the light n=1: exponential disk n=4: de Vaucouleurs profile r e and I e needed for scaling relations Bulge/disk decomposition possible only for high S/N ratios and/or low redshift Waddington et al. 2002

40. When SB fitting cannot be done… When galaxies are too faint for surface brightness fitting, it is possible to use quantitative morphological parameters such as Asymmetry (A) and Concentration (C) (Abraham et al. 1996)

41. CAS parameters The most used parameters are: Concentration (C), Asymmetry (A) and Clumpiness (S) (Conselice 2003)

42. Other parameters Bar fraction (fbar) and Concentration (Abraham et al. 1999)

43. Internal color maps If the S/N ratio is high enough and more photometric bands are available, it is possible to derive color maps of faint galaxies in order to study internal colors as tracers of recent or ongoing star formation activity (Menanteau et al. 2001) Ring of star form. Blue core Passive Passive + SF ? Passive

44. The morphological k-correction The morphological classification of star-forming galaxies depends STRONGLY on wavelength ! Evolutionary studies based on fixed-band observations are affected by this effect UVOptical UV Optical UV 2500 Å UV 3000 Å Optical UV Optical HST observations showing the effect Windhorst et al. 2002

45. H Tools (IV) Clustering analysis

46. Large scale structure & clustering Simulation of large scale structure Real galaxy distribution projected on the sky W(θ) -> ξ(r)=(r/r 0 ) -γ

47. Clustering “basics” 2-point angular correlation function w(θ): excess probability over a poissonian distribution of finding two galaxies in the two Solid angles dΩ 1 and dΩ 2 and separated by the angle θ Estimator based on the counts of objects binned in logarithmic distance intervals: [DD]= data-data sample, [DR]= data-random sample, [RR]= random-random sample (Landy & Szalay 1993). Biased to lower values with respect to the real w(θ). σ = “integral constraint” Assumed functional form (often it is adopted δ=0.8) The amplitude A of the real w(θ) can be estmated by fitting this function to the observed w b (θ)

48. Example of w(θ) estimates Daddi et al. 2000

49. From 2D to 3D clustering For small angles (θ<<1), both angular and spatial CF have power law shapes. ξ(r,z)= 3D 2-point CF, r 0 (z) = comoving correlation length at redshift z Limber equation: the knowledge of the redshift distribution allows to derive 3D clustering starting from 2D angular clustering. Parametrization of the redshift distribution z m = √2 z c = median redshift of the sample. Local universe: E/S0 more clustered than spirals

50. Observations vs. models Observed clustering is a powerful tool to study the formation and evolution of large scale structure Tracks: evolution of DM halos from (Mo & White 2002) SCUBA

51. H The need for surveys

52. Many open questions … z(max), primordial objects, proto-systems Cosmic star formation evolution Mass assembly evolution Dusty galaxies Large scale structure evolution Normal and Active galaxies co-evolution Origin of the Hubble sequence Comparison with models

54. Observing high-z galaxies: the 1996+ revolution HST ESO VLT JCMT Keck

55. H Let’s plan a galaxy survey

56. Survey of surveys … Salvato 2005 www.mpe.mpg.de/~mara/surveys

57. Planning a galaxy survey Different questions require different surveys Main parameters and ingredients of a survey: Band selection and sensitivity k-correction effects Dust extinction effects Limiting flux Photometric completeness and biases Color selection (if any) Telescope and instrument(s) Sky area coverage and cosmic variance effects Sample size Spectroscopic or photometric redshifts ? Number and wavelengths of photometric bands (for SEDs & zphot) Duration of the project Complementary multi-wavelength data

58. Our question: how and when galaxies assembled their mass ? We need a sample selection sensitive to galaxy masses Optical selection: sensitive to rest-frame ultraviolet light emitted by hot, short-lived, massive stars, thus probing star-forming galaxies and biased against quiescent or dust-obscured systems. Near- to mid-IR selection: sensitive to the light emitted by low-mass, long- lived stars, thus being sensitive to the stellar mass and less affected by dust extinction effects and by strong biases against some galaxy types.

59. SED shapes: UV vs. IR For stellar ages >10 Myr the shapes of spectra are very similar in the near-IR, but very different in the UV Sawicky 2001

60. We need filters which sample the rest-frame near-infrared at high redshift and with sufficient sensitivity to select galaxies with L ≤ L * Ks (2.2μm) for ground-based observations and z<2 3.5-8.0μm for space-based observations and z>2 (e.g. ISO, Spitzer, ASTRO-F) Bands and sensitivity Observed colors very different for different star formation histories The observed K-band mag show small variations !! M(stars)=10 11 Msun Kauffmann & Charlot 1998 Saracco et al. 2005

61. k-corrections R = photometric bandpass used in the observation Q= rest-frame bandpass where we want to know the absolute magnitude DM = distance modulus DL = luminosity distance ν o, ν e = observed and emitted frequency f ν = spectrum of the source (erg/s/cm 2 /Hz) g ν = flux of the zero-magnitude or “standard” source, e.g. Vega)

62. k-corrections: optical vs. near-IR Irr Sb E The advantage of K-band (2.2μm): very small k-correction effects up to z~2 and sensitivity to different galaxy types and to stellar masses. Cowie et al. 1994 Courtesy of L. Pozzetti

63. k-corrections (Spitzer vs. K-band) L. Pozzetti

64. k-corrections in the submm-mm Blain et al. 2002

65. Sensitivity to stellar masses (Spitzer vs. K-band) B. Lanzoni

66. Dust extinction effects 1μm1μm 0.32μm 0.1μm k(λ)=A 0 λ /E(B-V) E(B-V)=A 0 (B)-A 0 (V) Rest-frame optical-infrared radiation in MUCH less affected by dust extinction. Another advantage of infrared surveys Calzetti 2001

67. To summarize Samples selected at 2-8μm are optimal to study galaxy and mass assembly evolution thanks to their: (1) Sensitivity to stellar mass (2) Weak k-correction effects (3) Smaller dust extinction

68. Counts and sample size ~10 galaxies arcmin -2 to K=20 Current generation of near-IR imagers cover fields from ~5 to ~30 arcmin 2 (e.g. NTT+SOFI, VLT+ISAAC) New imagers cover up to 1 square degree (e.g. UKIRT+WFCAM, VISTA) Cimatti et al. 2002

69. Colors and depth In a sample with Ks<20, the faintest (reddest) galaxies have optical magnitudes down to R>26, I>25 (Vega). This sets the depth required for the optical photometry and/or optical spectroscopy to characterize the SEDs of the sample galaxies and to derive their photometric and/or spectroscopic redshifts. No near-IR multi-object spectrographs are currently available. MOS can be done only in the optical. Current limits for optical spectroscopy with 8-10m telescopes: R~25-26 (a few hours of integration time) Pozzetti et al. 2003

70. Field-to-field variations and cosmic variance This survey will find a high density of galaxies … This survey will find a deficit and will claim that this class of galaxy is rare… Very important to cover wide and/or independent fields Daddi et al. 2000

71. More on the cosmic variance Volumes sampled by some surveys Relative cosmic variance Significant field-to-field variations are present depending on the survey field size and on the clustering of different galaxy populations ! EROs (r 0 ~12 Mpc) B-dropouts r 0 ~3 Mpc Somerville et al. 2004

72. End of Lecture 1 !!!