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Formazione ed evoluzione delle galassie Scuola Normale Superiore

Formazione ed evoluzione delle galassie Scuola Normale Superiore Gennaio – Febbraio 2006. Andrea Cimatti INAF – Osservatorio Astrofisico di Arcetri (Firenze). Lecture 3 - Outline. Largest telescopes Atmospheric transmission Signal-to-noise ratio

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Formazione ed evoluzione delle galassie Scuola Normale Superiore

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  1. Formazione ed evoluzione delle galassie Scuola Normale Superiore Gennaio – Febbraio 2006 Andrea Cimatti INAF – Osservatorio Astrofisico di Arcetri (Firenze)

  2. Lecture 3 - Outline Largest telescopes Atmospheric transmission Signal-to-noise ratio Galaxy surveys: ingredients and problems Methods to find high-redshift galaxies How to find references (in the web): NASA ADS http//babbage.sissa.it (astrophysics)

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

  4. Main current telescopes useful in galaxy evolution studies SWIFT (gamma-ray bursts) XMM-Newton and Chandra X-ray Observatory GALEX (UV, 50cm diameter) 6-10m optical/NIR ground-based telescopes (Magellan, VLT, Gemini, Keck, Subaru, LBT, …) Hubble Space Telescope (optical & NIR, 2.5m diameter) Spitzer Space Telescope (85cm diameter, mid- to far-IR, 3-160μm) JCMT (15m diameter, submm/mm) IRAM (30m diameter, mm) IRAM Plateau de Bure (interferometer, mm) VLA and AT (radio)

  5. The atmospheric transmission H

  6. Sky emission and absorption in optical and near-infrared

  7. The signal-to-noise ratio (optical/NIR) N = number of electrons per bin [e/bin] F = flux (W/m^2/μm) P=hc/=photon energy Δi =filter width Δs =spectral resolution element (μm/bin) T = total integration time (seconds) E = efficiency S = mirror surface (m^2) Ω = solid angle seen by integration elem. [seeing] = arcsec n_pix=number of spatial pixels pix_scale= size of 1 pix in arcsec RON= Read-out noise DARK= dark current noise N_obj,N_sky= total number of counts • The fluxes of distant faint galaxies are much fainter that the sky background: • Sky brightness in R-band (7000 Å): 20.9 mag/arcsec^2 • High-z galaxies have typically R ~ 24-26 or fainter ! • background-limited regime S/N ~ √(T) Current observational limits: imaging: m(R) ~ 30 spectroscopy: m(R) ~ 25-26 For more details and test cases see: www.eso.org/observing/etc

  8. Planning a galaxy survey

  9. Planning a galaxy survey Survey = selection and observation of a sample of galaxies aimed at addressing one or more cosmological/astrophysical problems Different scientific cases 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 Synergy with theoretical groups  expectations, simulations, “mock” cat.

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

  11. An example: 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.

  12. 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

  13. Bands and sensitivity 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) Kauffmann & Charlot 1998 Saracco et al. 2005 M(stars)=1011 Msun The observed K-band mag show small variations !! Observed colors very different for different star formation histories

  14. 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 νo, νe = observed and emitted frequency fν = spectrum of the source (erg/s/cm2/Hz) gν = flux of the zero-magnitude or “standard” source, e.g. Vega)

  15. k-corrections: optical vs. near-IR The K-band (2.2μm) selection has small k-correction effects up to z~1-2 compared to the optical band, i.e. more unbiased sensitivity to different galaxy types. The same arguments can be applied to the Spitzer 3.6-8μm bands for higher redshifts. Irr Sb E Cowie et al. 1994

  16. Counts and sample size ~10 galaxies arcmin-2 to K=20 Current generation of near-IR imagers cover fields from ~5 to ~30 arcmin2 (e.g. NTT+SOFI, VLT+ISAAC) New imagers cover up to 1 square degree (e.g. UKIRT+WFCAM, VISTA) Cimatti et al. 2002

  17. 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

  18. Photometric completeness Completeness = fraction of galaxies detected in a survey as a function of limiting flux and galaxy properties. It can be estimated by randomly placing artificial galaxies in the image and deriving their recovery rate as a function of flux and morphology Artificial galaxies with de Vaucouleurs profile (b/a=0.7) Artificial galaxies with exponential profile (b/a=0.4) Cimatti et al. 2002

  19. Field-to-field variations and cosmic variance Daddi et al. 2000 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

  20. More on the cosmic variance Volumes sampled by some surveys Significant field-to-field variations are present depending on the survey field size and on the clustering of different galaxy populations ! EROs (r0~12 Mpc) Relative cosmic variance B-dropouts r0~3 Mpc Somerville et al. 2004

  21. How to select high-redshift galaxies

  22. How to find distant galaxies (1) Flux-limited sample + photometric redshifts and/or spectroscopic redshifts The choice of the wavelength can strongly bias the sample content by favoring the selection of galaxies which are bright at that wavelength For example: optical selection favors star-forming galaxies as it samples the rest-frame UV emission, and more strongly in B-band than in I-band. Near-infrared selection (if not deep enough) may be biased against very young star-forming (i.e. blue) galaxies which are faint at NIR wavelengths “Pure” flux-limited surveys are the “cleanest” and the ones with broadest applications But the spectroscopic identification is very time-consuming and not feasible at very faint magnitudes (e.g. R>26) The photometric redshift approach is a viable alternative, but it requires many filters (opt+NIR), very deep imaging, high accuracy photometry and a comparison/calibration with spectroscopic redshifts Dust extinction: critical in the optical, less in near- and mid-IR Examples: K20 (NIR+spectroscopy), VVDS (optical+spectroscopy), HDF and GOODS (optical/NIR + photo-z + spectroscopic redshifts), … many others

  23. VLT+ISAAC Ks-band mosaic of the GOODS-S field

  24. Example of a spectroscopic mask design FORS2 + MXU

  25. Example of multi-object spectroscopic raw data

  26. Example of sky-subtracted data

  27. The (big) problem of CCD fringing Fringing is caused by multiple reflections inside the CCD. At longer wavelengths, where thinned chips start to become transparent, light can penetrate through and be reflected from the rear surface. It then interferes with light entering for the first time. This can give rise to constructive and destructive interference and a series of fringes where there are minor differences in the chip thickness. For spectroscopic applications, fringing can render some thinned CCDs unusable, even those that have quite respectable QEs in the red. Thicker deep depletion CCDs , which have a much lower degree of internal reflection and much lower fringing are preferred by astronomers for spectroscopy.

  28. Removing fringing with “dithered” observations slit Spectrum + sky + fringing (A) A B galaxy Spectrum + sky + fringing (B) A-B B-A (A-B)+(B-A)S

  29. Example of dithered data l 

  30. Optical spectra FORS2 Early-type z=1.096 Early+emission z=0.735 Emission lines z=1.367 Absorption lines z-=1.725 Cimatti et al. 2002

  31. ISAAC 2D spectrum H-band Low-resolution Ha z=1.729 l

  32. Near-IR spectra ISAAC z=1.729 z=1.715 Cimatti et al. 2002

  33. Photometric redshifts Library of input template spectra From MUNICS web page (Drory et al.; Bender et al.)

  34. How to find distant galaxies (2) Flux-limited sample + color selection from broad-band filters Simple and “cheap” approach to find specific classes of galaxies Used with success bot in the optical and near-infrared Different criteria and methods are required to select different galaxy types in a complementary way Dust extinction: critical in the optical

  35. Broad-band color selections (1) Optical selection based on UGRiz filters (Steidel et al.; Adelberger et al.)

  36. Broad-band color selections (2)

  37. The Lyman-break technique Lyman-break technique (Steidel & Hamilton 1992; Steidel et al. 1996) Very successful (also for quasars) (3<z<7) Can be used for a wide redshift range But it works for galaxies with little dust extinction U-dropouts  z~3 B-dropouts  z~4 V,R-dropouts  z~5 I-dropouts  z~6 J-dropouts  z~10

  38. The LBG selection at z~3 and z~4 Giavalisco 2002

  39. Optical selection based on broad-band colors Magenta (“BM” selection): 1.5 < z < 2 Cyan (“BX” selection): 2 < z < 2.5 Yellow+Green (LBG selection): z ~ 3 Steidel et al. 2005 BM BX LBG

  40. The near-infrared view Dickinson et al. 2000

  41. The Extremely Red Object selection (EROs) • R-K>5 or I-K>4 is the observed color expected at z > 1 in case of • high formation redshift followed by passive evolution • or rapidly declining SFR • EROs trace the oldest envelope of galaxies at z>1 • However, very red colors are also expected in case of galaxies • with ongoing star formation and strong dust reddening non-evolving E/S0 Sab A(V)=1 mag A(V)=1.5 A(V)=2 Dashed lines: same n-e SEDs of left panel (no dust) n-e Sab n-e Scd n-e Irr Evolving models (solid lines) z_f=30 SFR~exp(t/τ) (τ=1,2,3 Gyr) McCarthy 2004

  42. J-K>2.3 – selected galaxies … J-Ks>2.3 (Vega system) allows to select “mature” galaxies atz>2(Franx et al. 03) These distant red galaxies (DRGs) are significantly redder than optically-selected galaxies in the same redshift range

  43. “BZK”: a new selection for 1.4<z<2.5 K20/CDFS field Capable to select jointly passive and star-forming gal. BZK = (Z-K)-(B-Z) > -0.2 BZK < -0.2 and Z-K > 2.5 reddening Also verified with simulated SED tracks Daddi et al. 2004

  44. Complementarity of optical and J-K>2.3 selections Selection region for optically-selected galaxies at z~2 (Adelberger et al. 03) DRGs do not fall in the optical color selection region … van Dokkum et al. 2004

  45. V-I vs I-K diagram Black: stars Green: z<1 Red: 1<z<1.5 Blue: z>1.5 McCarthy 2004

  46. Summary of most used broad-band color selections BM1<z<2 star-forming, low extinction (Adelberger et al. 2004) BX2<z<2.5 star-forming, low extinction (Adelberger et al. 2004) LBG3<z< 10+ star-forming, low extinction (Steidel et al. 1996) ERO(R-K>5, I-K>4, I-H>3) 1<z<2, old/passive or dusty star-forming (McCarthy 2004 review) DRG(J-K>2.3) 2<z<4, mostly dusty star-forming (Franx et al. 2003) BzK1.4<z<2.4, star-forming and old/passive, dust reddening-independent (Daddi et al. 2004) RJL2.5<z<4, similar to BzK (to be tested) Plus several other color-color diagram selection techniques

  47. How to find distant galaxies (3) Flux-limited sample + color or zphot selection from medium-band filters In principle it is an excellent approach No particular biases Equivalent to take very low-resolution spectra But the limited width of the filter bands require very long integration times to reach very faint magnitude levels with high photometric accuracy Unfrequently used to date, with the exception of COMBO-17 (optical) Never used nor planned in the near-infrared Dust extinction: critical in the optical Next steps: Subaru 8m, VST, …

  48. Medium-band filters Filter systems used in the COMBO-17 survey (12 medium-band + 5 broad-band) (Meisenheimer, Wolf et al.)

  49. How to find distant galaxies (4) Flux-limited sample + colors selection from narrow-band filters Finds galaxies with strong emission lines and line-emitting halos/clouds Used in optical and near-infrared It is important to use filters non affected by OH sky lines The problem is the limited redshift range (volume) surveyed Typically Δz ~ 0.1 Spectroscopic confirmation is always required ! New opportunities are offered by Integral Field Units or Tunable Filters

  50. Narrow-band imaging in the optical OH sky emission lines “Clean” window

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