Galaxy Kinematics and Cosmology from Accurately Modeling the Redshift-Space Galaxy Clustering. Zheng Zheng( 郑政 ) University of Utah

Transcription

1 Galaxy Kinematics and Cosmology from Accurately Modeling the Redshift-Space Galaxy Clustering Zheng Zheng( 郑政 ) University of Utah National Astronomical Observatories, CAS July 12, 2017

2 Ben Bromley Professor Ph.D., Dartmouth College, 1994 Planet formation; formation & evolution of black holes; galactic dynamics; large-scale structure of the universe; computational & statistical method in astrophysics Kyle Dawson Associate Professor Ph.D., Cornell Univ., 2004 Observational cosmology; astronomical instrumentation; supernovae; large-scale structure; spectroscopic surveys Paolo Gondolo Professor Ph.D., UCLA, 1991 Nature of dark matter & dark energy; highenergy cosmic neutrinos Inese Ivans Assistant Professor Ph.D., Univ. of Texas at Austin, 2002 Stellar spectroscopy; origins of chemical elements; stellar populations; formation & evolution of galaxies, including the Milky Way David Kieda Professor Ph.D., Univ. of Pennsylvania, 1989 Experimental high energy astrophysics; energetic phenomena in compact objects; gamma ray astronomy; cosmic ray physics Pearl Sandick Assistant Professor Ph.D., Univ. of Minnesota, 2008 Dark matter; particle astrophysics & cosmology; supersymmetry phenomenology; physics beyond the standard model Anil Seth Assistant Professor Ph.D., Univ. of Washington, 2006 Observations of nearby galaxies; formation of galaxy nuclei & black holes; galaxy histories from resolved stellar populations & star clusters Wayne Springer Associate Professor Ph.D., Univ. of Maryland, 1991 Ultra high energy cosmic ray physics; cosmic ray detectors; astroparticle physics; observational astronomy Daniel Wik Assistant Professor Ph.D., Univ. of Virginia, 2010 X-ray astronomy; galaxy clusters; galaxies; black holes; cosmology Gail Zasowski Assistant Professor Ph.D., Univ. of Virginia, 2012 Galactic archeology; the Milky Way; stellar populations; the interstellar medium Zheng Zheng Associate Professor Ph.D., Ohio State Univ., 2004 Cosmology, large-scale structure, & galaxy clustering; galaxy formation & evolution; high-redshift star forming galaxies; radiative transfer of Lymanalpha photons & application in astrophysics The University of Utah Department of Physics & Astronomy University of Utah Physics & Astronomy Institutional member of SDSS-III, SDSS-IV, AS4 Data center for SDSS-IV Involved in DESI Cosmology Galaxy formation Galactic and stellar astronomy Planet formation X-ray/Gamma-ray astronomy Particle astrophysics Cosmic rays

3 Great Salt Lake Salt Lake City nationalgeographic.com

4 Galaxy Kinematics and Cosmology from Accurately Modeling the Redshift-Space Galaxy Clustering Main Collaborators Hong Guo (SHAO) Jia-Ni Ye (SHAO) Haojie Xu (Utah) Kevin McCarthy (Utah) Xiaoju Xu (Utah) Idit Zehavi (CWRU)

5 Inflation Quantum Fluctuations Reionization First Stars and Galaxies Dark Energy Accelerated Expansion Dark Matter Dark Energy 13.8 billion years

6 Dark Matter Halo Formation image courtesy: V. Springel

7 Galaxy Formation accretion heating cooling star formation star formation feedback supermassive black hole growth supermassive black hole feedback mergers...

8 Observation: Bright Side Theory: Dark Side image courtesy: M. Tegmark Galaxy Formation Gastrophysics gas cooling, gas dynamics, star formation, feedback,... image courtesy: V. Springel Dark Matter Halo Formation Gravity

9 Cosmology Galaxy Formation Physics gas dynamics Galaxy Formation star / AGN feedback star formation known unknowns gas cooling unknown unknowns known knowns Dark Matter Halo Population known unknowns Halo Occupation Distribution (HOD) Galaxy Clustering

10 Two-point Correlation Function (2PCF) of Galaxies Excess probability w.r.t. random distribution of finding galaxy pairs at a given separation galaxies random

11 Two-point Correlation Function (2PCF) of Galaxies Baryon Accoustic Oscillation (BAO), standard ruler Small-scale shape, neutrino mass Broad-band shape, cosmological parameters Small- and intermediate-scale shape and amplitude, galaxy-halo connection Anderson et al. (2012)

12 3D Two-point Correlation Function of Galaxies Zehavi, ZZ, et al. (2011) r π rp Guo, ZZ, et al. (2015a)

13 Galaxy comoving with the expansion Distance Cosmological Redshift Peculiar velocity of the galaxy velocity w.r.t. the comoving frame => Doppler redshift v Observed Redshift: Cosmological Redshift + Doppler Redshift Distance inference: distorted by the Doppler redshift from galaxy peculiar motion

14 Large-Scale Linear Redshift-Space Distortion (Kaiser 1987) v 1 + a r v =0 (continuity) Probe combination of structure growth rate and fluctuation amplitude (gravity, dark energy) Real Space Redshift Space

15 Small-Scale Nonlinear Redshift-Space Distortion (Finger-of-God Effect) v Probe galaxy kinematics inside dark matter halos (galaxy formation and evolution) Real Space Redshift Space

16 Redshift-Space Distortion (Gravitational Distortion) Guo, ZZ, et al. (2015a) Galaxy Formation and evolution: kinematics of galaxies inside halos Cosmology: amplitude and growth rate of matter density fluctuation

17 Projected Two-point Correlation Function of Galaxies redshift-space distortion effect removed essentially the real-space clustering 1-halo term 2-halo term Central Satellite Galaxy Pair Counts HOD (Galaxy-Halo Relation)

18 HOD Modeling of the SDSS Galaxy Clustering Luminosity Dependence z~0.1 bright HOD faint 2PCF Mass-Luminosity Mass/Luminosity Zehavi, ZZ, et al. (2011) 17x faint bright

19 Redshift-Space Galaxy Clustering Small scales: FoG galaxy kinematics inside virtualized structures (halos) Large scales: Kaiser effect structure growth rate The simple model is not accurate.

20 High Precision Galaxy Clustering Measurements ~ accuracy of analytic models of real-space 2PCFs (e.g., Tinker+05, van den Bosch+13)

21 Difficulties in Developing Accurate Models of Galaxy Clustering non-linear evolution of matter power spectrum scale dependence of halo bias halo exclusion effect nonsphericity of halos halo alignment (Zheng04, Tinker+05, van den Bosch+13)

22 1 T 1 Difficulties in Developing Accurate Models of Galaxy Clustering P (v r,v t r, M 1,M 2 ) Distribution of halo-halo (radial and transverse) pairwise velocity (e.g., Tinker 2007, Reid & White 2011, Zu & Weinberg 2013) Reid & White (2011) Zu & Weinberg (2013) Figure 3. Joint probability distributions of radial and tangential velocities P (v r,v t ) from the simulation (top panels) and the best fit using our GIKmodel(bottom panels), in four different radial bins marked at the bottom of each panel. The colour scales used by panels in the same column are identical, indicated by the colour bar on top.

23 Model Galaxy Clustering with N-body Simulations Populate halos with galaxies according to HOD/CLF to form mock Measure 2PCFs from the mock as the model prediction Credit: Springel+(2005) e.g., White+(2011), Parejko+(2013) halotools (Hearin+2016)

24 More Efficient Simulation-Based Clustering Modeling n g = i [ N cen (M i ) + N sat (M i ) ] n i, 1+ξ 1h gg (r) = + i ξ 2h gg (r) = + i n i n 2 g i j + i j i j 2 n i n 2 g N cen (M i )N sat (M i ) f cs (r; M i ) N sat (M i )[N sat (M i ) 1] f ss (r; M i ) n i n j n 2 g 2 n i n j n 2 g n i n j n 2 g N cen (M i ) N cen (M j ) ξ hh,cc (r; M i,m j ) N cen (M i ) N sat (M j ) ξ hh,cs (r; M i,m j ) N sat (M i ) N sat (M j ) ξ hh,ss (r; M i,m j )

25 More Efficient Simulation-Based Clustering Modeling n g = i HOD Halo Properties [ N cen (M i ) + N sat (M i ) ] n i, Mass Function 1+ξ 1h gg (r) = + i ξ 2h gg (r) = + i n i n 2 g i j + i j i j 2 n i n 2 g N cen (M i )N sat (M i ) f cs (r; M i ) N sat (M i )[N sat (M i ) 1] f ss (r; M i ) n i n j n 2 g 2 n i n j n 2 g n i n j n 2 g N cen (M i ) N cen (M j ) ξ hh,cc (r; M i,m j ) N cen (M i ) N sat (M j ) ξ hh,cs (r; M i,m j ) N sat (M i ) N sat (M j ) ξ hh,ss (r; M i,m j ) Profile Clustering

26 Accurate and Efficient Halo-Based Galaxy Clustering Modeling with Simulations Accurate - equivalent to populating galaxies into dark matter halos and using the (mean) mock 2PCF measurements as the model prediction - no finite-bin-size effect (same binning and integration scheme as measurements); residual RSD automatically accounted for Efficient - no need for the construction of mocks and the measurement of the 2PCF from the mocks - independent of simulation size - efficient exploration of the parameter space (e.g., MCMC) Extension to subhalos (SCAM), halo variables other than mass, and other clustering statistics (Neostein+2011, Neistein & Khochfar2012, Zheng & Guo 2016, Guo+2015)

27 An Accurate and Efficient Simulation-based Model for Redshift-Space Galaxy Two-Point Correlation Function ZZ & Guo (2016)

28 ZZ & Guo (2016)

29 ZZ & Guo (2016)

30 one-halo total ZZ & Guo (2016)

31 one-halo total ZZ & Guo (2016)

32 one-halo total ZZ & Guo (2016)

33 one-halo total ZZ & Guo (2016)

34 one-halo total two-halo total ZZ & Guo (2016)

35 total one-halo total two-halo total ZZ & Guo (2016)

36 An Accurate and Efficient Simulation-based Model for Redshift-Space Galaxy Two-Point Correlation Function Projected Monopole Quadrupole Hexadecapole ZZ & Guo (2016)

37 Modeling Redshift-Space Galaxy Clustering Choose the reference frame to define galaxy velocity bias - halo core frame - halo bulk velocity frame (more appropriate for large, low-res simulations) Behroozi et al. (2013) Account for galaxy redshift errors (Gaussian-Convolved Laplace Distribution) Guo, ZZ, et al. (2015c)

38 Constraining Galaxy Kinematics inside Halos Velocity bias In the halo frame c = v cen DM s = sat DM Guo, ZZ, et al. (2015a)

39 BOSS (Baryon Oscillation Spectroscopic Survey) z~0.5 massive galaxies

40 Measuring and Modeling the Redshift-Space Galaxy Clustering Guo, ZZ, et al. (2015a) Projected Monopole Quadrupole Hexadecapole

41 Galaxy Kinematics inside Halos satellite velocity bias BOSS Galaxies (( )) central velocity bias Guo, ZZ, et al. (2015a) The central galaxy in a halo is not at rest w.r.t. the halo.

42 Similar Results of Galaxy Motion from Redshift-Space 3-point Correlation Functions r2 r 1 satellite velocity bias central velocity bias Guo, ZZ, et al. (2015b)

43 SDSS Main Galaxy Sample (z~0.1) Projected Monopole Quadrupole Hexadecapole Guo, ZZ, et al. (2015c)

44 Velocity Bias of SDSS Main Galaxies (z~0.1) Guo, ZZ, et al. (2015c) faint samples bright samples In broad agreement with results based on galaxy groups (van den Bosch+2005; Skibba+ 2011)

45 Velocity Bias of SDSS Main Galaxies (z~0.1) faint bright faint bright Guo, ZZ, et al. (2015c)

46 Velocity Bias of SDSS Main Galaxies (z~0.1) pairwise infall velocity cen gal velocity dispersion faint bright In lower mass halos, central galaxies and halos are more mutually relaxed, consistent with an overall earlier formation and thus more time for relaxation.

47 Evolution of Velocity Bias of Luminous Central Galaxies z~0.1 z~0.5 * faint bright No evidence for evolution (from z~0.5 to z~0.1) for velocity dispersion of luminous central galaxies central galaxies and host halos may have been constantly disturbed by galaxy and halo mergers?

48 Velocity Bias in the Illustris Simulation Ye, Guo, ZZ, & Zehavi (2017)

49 Velocity Bias in the Illustris Simulation Ye, Guo, ZZ, & Zehavi (2017) cen v bias more affected by halo accretion/merger sat v bias more affected by dynamics inside halos

50 Small- and intermediate-scale redshift-space distortions help tighten cosmological constraints. + 1 a r v =0 (continuity) Probe structure growth rate Test theories of gravity Constrain dark energy Dawson, et al. (2015)

51 Assembly Effect on Halo Clustering and Kinematics v12 at 6Mpc/h Xu & ZZ (in prep)

52 Tightening Cosmological Constraints from Small- and Intermediate-Scale Redshift-Space Distortions f d ln D d ln a large-scale 3D redshift 2PCF amplitude => b 8 shape => f b } => f 8 insensitive to assembly bias Dawson, et al. (2015)

53 Assembly Effect on fσ8 Constraint from Small Scales w/ assembly bias w/o assembly bias [McEwen & Weinberg (2016) on matter correlation from galaxy correlation function and galaxy lensing] McCarthy, ZZ, & Guo (in prep)

54 Summary accurate and efficient modeling of small- and intermediate-scale redshift-space galaxy clustering by tabulating necessary information of halos in N-body simulations redshift-space clustering modeling of BOSS CMASS and SDSS Main galaxies to constrain galaxy kinematics inside halos (and tighten fσ8 constraints), with inferred velocity bias in broad agreement with predictions of hydro simulations influence of assembly bias on fσ8 constraints and halo assembly on both halo clustering and kinematics

55 Test the Velocity Bias Constraints Combinations of observables Fiber-collision correction HOD parameterization

56 Test the Velocity Bias Constraints (assembly bias) Effect of Spatial Distribution Profile of Satellites

57 Similar Results of Galaxy Motion from Redshift-Space 3-point Correlation Functions r2 r 1 satellite velocity bias BOSS Galaxies (z~0.5) central velocity bias Guo, ZZ, et al. (2015b)

58 An Accurate Method to Correct for the Fiber Collision Effect Guo, Zehavi, & ZZ (2012)

59 Modeling RSD of BOSS CMASS Galaxies

60 Modeling Redshift Error Distribution Gaussian-Convolved Laplace Distribution

61 Modeling Redshift Error Distribution Distribution of Sample Variance of Redshift Errors