1. The Physics of Galaxy Formation Institute for Computational Cosmology University of Durham Michael Balogh

2. M31 “Andromeda” galaxy

3. Hubble Deep Field/HST

4. In the beginning …

5. WMAP (Bennett et al. 2003) In the beginning… z ~ 1000

6. Perturbation Growth Growth of linear perturbations a function of  M and   WMAP

7. In the beginning …

8. Spergel et al. 2003 Mean and 68% confidence errors Amplitude of fluctuations A=0.83  0.08 Spectral index at k=0.05 Mpc -1 n s =0.93  0.03 Derivative of spectral index dn s /dlnk=-0.031  0.017 Hubble constant h=0.71  0.03 Total density/critical density  tot =1.02  0.04 Matter density/critical density  m =0.27  0.04 Baryon density/critical density  b =0.044  0.004 Age of the Universe t o =13.7  0.2 Gyr Reionization Redshift z r =17  4 Matter power spectrum normalization  8 =0.84  0.04 Decoupling redshift z dec =1089  1 Age of the universe at decoupling t dec =379  7 kyr Thickness of surface of last scatter  z dec =195  2 Redshift of matter-radiation equality z eq =3233  200 Fit to the WMAP, CBI, ACBAR, 2dFGRS, SN1a and Lyman  forest data

9. Spergel et al. 2003 Mean and 68% confidence errors Amplitude of fluctuations A=0.83  0.08 Spectral index at k=0.05 Mpc -1 n s =0.93  0.03 Derivative of spectral index dn s /dlnk=-0.031  0.017 Hubble constant h=0.71  0.03 Total density/critical density  tot =1.02  0.04 Matter density/critical density  m =0.27  0.04 Baryon density/critical density  b =0.044  0.004 Age of the Universe t o =13.7  0.2 Gyr Reionization Redshift z r =17  4 Matter power spectrum normalization  8 =0.84  0.04 Decoupling redshift z dec =1089  1 Age of the universe at decoupling t dec =379  7 kyr Thickness of surface of last scatter  z dec =195  2 Redshift of matter-radiation equality z eq =3233  200 Fit to the WMAP, CBI, ACBAR, 2dFGRS, SN1a and Lyman  forest data

10. Spergel et al. 2003 Mean and 68% confidence errors Amplitude of fluctuations A=0.83  0.08 Spectral index at k=0.05 Mpc -1 n s =0.93  0.03 Derivative of spectral index dn s /dlnk=-0.031  0.017 Hubble constant h=0.71  0.03 Total density/critical density  tot =1.02  0.04 Matter density/critical density  m =0.27  0.04 Baryon density/critical density  b =0.044  0.004 Age of the Universe t o =13.7  0.2 Gyr Reionization Redshift z r =17  4 Matter power spectrum normalization  8 =0.84  0.04 Decoupling redshift z dec =1089  1 Age of the universe at decoupling t dec =379  7 kyr Thickness of surface of last scatter  z dec =195  2 Redshift of matter-radiation equality z eq =3233  200 Fit to the WMAP, CBI, ACBAR, 2dFGRS, SN1a and Lyman  forest data

11. Spergel et al. 2003 Mean and 68% confidence errors Amplitude of fluctuations A=0.83  0.08 Spectral index at k=0.05 Mpc -1 n s =0.93  0.03 Derivative of spectral index dn s /dlnk=-0.031  0.017 Hubble constant h=0.71  0.03 Total density/critical density  tot =1.02  0.04 Matter density/critical density  m =0.27  0.04 Baryon density/critical density  b =0.044  0.004 Age of the Universe t o =13.7  0.2 Gyr Reionization Redshift z r =17  4 Matter power spectrum normalization  8 =0.84  0.04 Decoupling redshift z dec =1089  1 Age of the universe at decoupling t dec =379  7 kyr Thickness of surface of last scatter  z dec =195  2 Redshift of matter-radiation equality z eq =3233  200 Fit to the WMAP, CBI, ACBAR, 2dFGRS, SN1a and Lyman  forest data

12.  stars = 0.0014 ± 0.00013  >95% of baryons are dark The matter budget: stars Cole et. al 2002

13. The baryon budget: hot gas Coma: XMM-Newton Observatory

14. The baryon budget: stars Coma: XMM-Newton ObservatoryComa cluster The baryon budget: hot gas

15. Coma: XMM-Newton Observatory

16. Remaining dark matter (~84%) Stars in galaxies (~1%) Hot gas between galaxies (~15%) The matter budget: clusters  M ≈ 0.23  b ≈ 0.04  * ≈ 0.003

17. The baryon budget Too cool to emit observable X-ray radiation. We know it exists, but can’t directly measure how much there is ? ? Stars in Galaxies (~5%) Gas in Galaxies (~2%) Gas in Clusters (~7%) Gas in Groups Intercluster Gas

18. Ignore the lights… Most of the baryons are invisible Most of the matter is non-baryonic, dark, and weakly interacting Most of the energy is not matter

19. Ignore the lights… Most of the baryons are invisible Most of the matter is non-baryonic, dark, and weakly interacting Most of the energy is not matter

20. you are here

21. you are here

22. The easy part: Dark matter

23. University of Durham Institute for Computational Cosmology 150 Mpc/h dalla Vechia, Jenkins & Frenk

24. University of Durham Institute for Computational Cosmology 3 Mpc/h dalla Vechia, Jenkins & Frenk

25. The hard part: baryons

26. Baryonic Physics Radiative cooling Radiative cooling Invisible baryons: ~10 6 K

27. Mergers Barnes (1992)

28. Mergers

29. Baryonic Physics Radiative cooling Radiative cooling

30. The cooling catastrophe Cooling occurs primarily through bremsstrahlung radiation, so t cool  T 1/2  -1 The typical density of haloes is higher at early times:   (1+z) 3 Thus, gas cools very efficiently in small haloes at high redshift. White & Frenk (1991) Balogh et al. (2001)

31. Why so few stars? Balogh et al. (2001) Observations imply  * /  b  0.05 f cool 0.1 0.6 0.5 0.4 0.3 0.2 Fraction of condensed gas in simulations is much larger, depending on numerical resolution Pearce et al. (2000) Lewis et al. (2000) Katz & White (1993) kT (keV) 110

32. Galaxy Luminosity Function Benson et al. 2003 Overcooling leads to the formation of hundreds more small galaxies than are observed.

33. Supernova feedback? M82/Subaru TelescopeM82/Chandra Observatory

34. Baryonic Physics Radiative cooling Radiative cooling Feedback

35. Details z > 1: Feedback z < 1: Environment

36. z > 1: Feedback

37. Why are groups underluminous? If cluster structure were self-similar, then we would expect L  T 2 Observations disagree, but why? Preheating by supernovae & AGNs?

38. Why are groups underluminous? If cluster structure were self-similar, then we would expect L  T 2 Observations disagree, but why? Preheating by supernovae & AGNs? 10 1 kT (keV) 40 41 42 43 44 45 46 log 10 L x (ergs s -1 ) L  T 2

39. K 0 =400 keV cm 2 Isothermal model M=10 15 M 0 Preheated gas has a minimum entropy that is preserved in clusters (Kaiser 1991; Balogh et al. 1999) Definition of S:  S =  (heat) / T Equation of state:P = K  5/3 Relationship to S:S = N ln K 3/2 + const. Convective Stability: dS/dr  0 Useful Observable: Tn e -2/3  K Only radiative cooling can reduce Tn e -2/3 Only heat input can raise Tn e -2/3 K o =400 keV cm 2 300 200 100

40. Balogh, Babul & Patton 1999 Babul, Balogh et al. 2002 log 10 L X [ergs s -1 ] kT [keV] 10 1 0.1 40424446 Isothermal model Preheated model K o =400 keV cm 2

41. Sunyaev-Zeldovich Effect Gomez et al. 2003 Decrement: 150 GHzIntermediate: 220 GHz Increment: 275 GHz 35’ Abell 3266 (Inverse-Compton scattering of CMB photons off hot electrons in the ICM)

42. S -y 0 relation y 0 -T X relation Constraints on “entropy” floor: S -y 0 K 0 =540 keV cm 2 y 0 -T X K 0 =300 keV cm 2 McCarthy et al. 2003 Sunyaev-Zeldovich Effect

43. Does supernova feedback work? Local SN rate ~0.002/yr (Hardin et al. 2000; Cappellaro et al. 1999) An average supernova event releases ~10 44 J Assuming 10% is available for heating the gas over 12.7 Gyr, total energy available is 2.5x10 50 J This corresponds to a temperature increase of 5x10 4 K To achieve an entropy floor K 0  T/  2/3 :  /  avg = 0.28 (K 0 /100 keV cm 2 ) -3/2 Consider the energetics for 10 11 M sun of gas: SN energy too low by at least a factor ~50

44. What about active galaxies? Perseus Cluster & 3C 84 Perseus Cluster / Chandra 10 kpc

45. Bubbles in the Intracluster Medium Quilis, Bower & Balogh 2001

46. Bubbles in the Intracluster Medium Quilis, Bower & Balogh 2001 Effective at disrupting cooling in the core, over the ~50 Myr lifetime of the bubble

47. The AGN solution? There is ~100 times more energy available in AGN than in supernovae Proven effective at disrupting cooling flows Details of how this energy is coupled to the surrounding gas are still uncertain

48. z < 1: Galaxy Ecology

49. B) External? Hierarchical build-up of structure inhibits star formation A) Internal? i.e. gas consumption and “normal” aging Steidel et al. 1999 SFR ~ (1+z) 1.7 (Wilson, Cowie et al. 2002) Why Does Star Formation Stop?

50. Galaxy clusters: the end of star formation?

51. Additional physics? Additional physics? Ram-pressure stripping (Gunn & Gott 1972) Collisions / harassment (Moore et al. 1995) “Strangulation” (Larson et al. 1980; Balogh et al. 2000)

52. Additional physics? Additional physics? Ram-pressure stripping (Gunn & Gott 1972) Collisions / harassment (Moore et al. 1995) “Strangulation” (Larson et al. 1980; Balogh et al. 2000) Quilis, Moore & Bower 2000

53. Additional physics? Additional physics? Ram-pressure stripping (Gunn & Gott 1972) Collisions / harassment (Moore et al. 1995) “Strangulation” (Larson et al. 1980; Balogh et al. 2000)

54. Additional physics? Additional physics? Ram-pressure stripping (Gunn & Gott 1972) Collisions / harassment (Moore et al. 1995) “Strangulation” (Larson et al. 1980; Balogh et al. 2000)

55. Galaxy Transformation in the 2DF survey A1620 Rvir (data extracted over ~7 deg field) Data for 17 Abell-like clusters Covers velocity dispersions 500 km/s - 1100 km/s Region out to > 20 Rvir extracted from the survey Major advantages :  ● Star formation rate measured from H   ● Complete redshift information - no need to subtract background!  ● Compare with surrounding field directly 1 degree

56. Abell 2390 (z~0.23) 3.6 arcmin R image from CNOC survey (Yee et al. 1996)

57. H  in Abell 2390 3.6 arcmin Balogh & Morris 2000

58. 300 200 100 0 -100 -200 -300 -200-1000100200  Dec  RA AC114 (z=0.31) (Couch, Balogh et al. 2001)

59. Nod & Shuffle: LDSS++ (AAT) band-limiting filter + microslit = ~800 galaxies per 7’ field

60. H  in Rich Clusters at z~0.3 Balogh et al. 2002 MNRAS, 335, 110 Couch, Balogh et al. 2001 ApJ 549, 820 LDSS++ with nod and shuffle sky subtraction, on AAT (Field)

61. Timescales Use numerical model of infall to estimate timescale for disruption of SFR Radial gradients in CNOC clusters suggest  ~2 Gyr Suppressed star formation within several Mpc of cluster centre! What environment is responsible? Balogh, Navarro & Morris 2000 Diaferio et al. 2001

62. The 2dFGRS and SDSS 2dF Galaxy redshift survey: – spectra and redshifts for 220 000 nearby galaxies – only photographic plate photometry Sloan digital sky survey: – goal is spectra for 1 million galaxies, with digital photometry (ugriz) – Early data release contained 50 000 galaxies

63. SFR-Density Relation in the 2dFGRS Lewis, Balogh et al. 2002 MNRAS 334, 673 Field Normalised star formation rate measured from H  in 17 nearby clusters Identified a critical density of ~1 Mpc -2, where environmental effects become important This corresponds to low density groups in the infall regions of clusters

64. SFR-Density in the SDSS Star Formation Rate (M o /yr) Galaxy Surface Density (Mpc -2 ) Median 75 th percentile Gomez et al. (2003) Field 75 th percentile Field median

65. Importance of Environment Star formation is inhibited in only moderately overdense (hence common) environments Likely due to a relatively slow process; not ram pressure stripping Impact on global evolution is still unknown

66. What Next?

67. Tying star formation to structure growth Groups Clusters

68. Local Groups in the 2dFGRS/SDSS Based on friends-of-friends catalogue (V. Eke) Mean SFR appears to be suppressed in all galaxy associations at z=0!

70. CNOC2 Groups at z~0.45 Deep spectroscopy with LDSS-2 on Magellan 1 (~30 groups) Infrared (Ks) images from INGRID Combined with CNOC2 multicolour photometry and spectroscopy, we can determine group structure, dynamics, stellar mass, and star formation history.

71. LDSS2 on Magellan [OII]

72. CNOC2 Groups at z~0.45 Preliminary results based on only 12 CNOC2 groups Have observed >30 groups to date Balogh et al. 1997

73. The CFHLS SurveyAreaFiltersDepth Total (ks) Strategy Total nights Wide Synoptic 1 8x9 2 7x7 u*25.56 162 g’26.52.5 r’25.721 early 1 3a later i’25.54.3 z’24.07.2 Deep Synoptic 4 1x1u*27118.85.25 nights per run, 5 runs a year for each field 202 g’28.4118.8 r’28237.6 i’27.8475.2 z’26237.6

74. The CFHLS identify clusters and groups to z=1. Expect ~50 clusters at 1<z<1.5 in Wide Synoptic Survey Overlap with XMM Large Scale Survey allows analysis of X-ray properties Requires spectroscopic and NIR follow-up

75. Galaxy formation theory Need self-consistent feedback model which explains: –galaxy luminosity function –cluster/group X-ray properties –cold fraction of baryons This likely requires coupling energy output of AGN to surrounding material. Detailed work is only now beginning.

76. Summary Underlying galaxy formation model is fairly well established However, it is dominated by unknown feedback processes. Wealth of data (esp. from Chandra and XMM-Newton) are shedding light on this, now Importance of additional physics in dense environments is currently unknown, but will be established with the completion of large surveys at z~1

77. Benson et al. 2002 Z=5

78. Benson et al. 2002 Z=3

79. Benson et al. 2002 Z=2

80. Benson et al. 2002 Z=1

81. Benson et al. 2002 Z=0.5

82. Benson et al. 2002 Z=0

83. Gas cooling in SPH and semianalytics SPH simulations Semi-analytics Helly et al. (2002)