Probing growth of cosmic structure using galaxy dynamics: a converging picture of velocity bias. Hao-Yi Wu University of Michigan

Transcription

1 Probing growth of cosmic structure using galaxy dynamics: a converging picture of velocity bias Hao-Yi Wu University of Michigan

2 Galaxies are not necessarily test particles

3 Probing dark energy with growth of structure Huterer et al. (arxiv: ) Snowmass white paper Simulation: Virgo Consortium Galaxy cluster counts Galaxy clustering (redshift-space distortions) Growth is the key to test modified gravity models!

4 Galaxies are not necessarily test particles Galaxy velocity bias b v = Velocity dispersion of galaxies (σ gal) Velocity dispersion of dark matter (σ DM ) How well do we need to understand b v in order to get unbiased cosmological parameters? How well can we understand b v with current simulations?

5 From galaxy surveys to parameter constraints Surveys Galaxy cluster abundance Galaxy clustering (power spectrum) Modeling Constraints Cosmic acceleration, modified gravity, primordial non- Gaussianity, etc. N cl (M obs, z) = Z Geometry dv c Z Growth dm n h (M, z)p(m obs M) Mass function Mass-observable relation

6 From galaxy surveys to parameter constraints Surveys Galaxy cluster abundance Galaxy clustering (power spectrum) Modeling Constraints Cosmic acceleration, modified gravity, primordial non- Gaussianity, etc. N cl (M obs, z) = P 1h gal(k) / Z Z Geometry dv c Z Growth Mass function Growth dm n h (M, z)p(m obs M) Mass function Number of galaxy pairs Mass-observable relation dm n h (M, z)hn gal (N gal 1)iu 2 profile(k)r(k, v) Density profile Velocities of galaxies

7 From galaxy surveys to parameter constraints Surveys Galaxy cluster abundance Galaxy clustering (power spectrum) Modeling Constraints Cosmic acceleration, modified gravity, primordial non- Gaussianity, etc. N cl (M obs, z) = P 1h gal(k) / Z Z Geometry dv c Z Growth Mass function Growth dm n h (M, z)p(m obs M) Mass function Number of galaxy pairs Mass-observable relation dm n h (M, z)hn gal (N gal 1)iu 2 profile(k)r(k, v) Density profile Velocities of galaxies

8 Small-scale redshift-space distortion (The Fingers-of-God effect) Real Space Redshift Space B A B A see e.g. Dodelson s textbook Virial motion of galaxies suppresses small-scale clustering

9 Small-scale clustering includes rich information Solid: no prior; dashed: Planck prior no nuisance parameters halo Zheng HOD parameters 2 para for cen; 5 para for sat k NL 1-halo dominated P(k ; z = 0.41) halo p (w a) (wp) without RSD with RSD k[h/mpc] Planck prior k max [h/mpc] Wu and Huterer, MNRAS ( )

10 Velocity bias can potentially dominate the systematic error in galaxy power spectrum Fractional bias in P(k) scales used for cosmology Wu and Huterer MNRAS ( )

11 How well can we understand velocity bias using current simulations?

12 N-body simulation Rhapsody MHD simulation Magneticum (Wu, Hahn, Wechsler et al. 2013) (Klaus Dolag) used in this work Zoom-in simulations of clusters Mass resolution: 1.3x108 M /h 96 clusters of mass 6x1014 M /h (z=0), 8142 progenitors (0 z 2) Collisionless, gravity only Rich subhalo/galaxy statistics Limited input physics Full cosmological volume Mass resolution: 6.9x108 M /h 46 clusters above 1014 M /h Star formation, AGN feedback, magnetic fields, etc. Rich input physics Limited statistics (so far)

13 Galaxy tracers in N-body simulations M pk or V pk M 0 or V 0 accretion occurs Subhalo properties include: V max : maximum circular velocity (GM/r max ) 1/2 ; proxy for subhalo mass V 0 (z=0): affected by stripping V pk (peak): unaffected by stripping; correlated with galaxy luminosity and stellar mass

14 In N-body sims, using subhalos current mass leads to too high velocity bias bv = σgal/σdm bv = gal/ DM N-body only DM-v 0 Massive/bright galaxies slow (dyn. friction) Low-mass/faint galaxies fast (stripping) Strong tidal stripping can remove slow galaxies and lead to higher velocity bias 0.7 DM-v pk Random N brightest galaxies in a halo N brightest galaxies in a cluster Wu, Hahn, Evrard et al. ( , MNRAS)

15 Using v pk in N-body sims gives results similar to hydro simulations bv = σgal/σdm bv = gal/ DM DM-v 0 DM-v pk Hydro-v 0 Galaxies in hydro simulations suffer less stripping less velocity bias v 0 in hydro sims behaves like v pk in N-body sims 0.7 Hydro-M star Random N brightest galaxies in a halo N brightest galaxies in a cluster Wu, Hahn, Evrard et al. ( , MNRAS)

16 Using v pk in N-body sims gives results similar to hydro simulations Be careful when using subhalo mass in N-body simulations! bv = σgal/σdm bv = gal/ DM DM-v 0 DM-v pk Hydro-v 0 Galaxies in hydro simulations suffer less stripping less velocity bias v 0 in hydro sims behaves like v pk in N-body sims 0.7 Hydro-M star Random N brightest galaxies in a halo N brightest galaxies in a cluster Wu, Hahn, Evrard et al. ( , MNRAS)

17 Comparing with results from the literature bv = σgal/σdm bv = gal/ DM N-body-v 0 N-body-v pk Hydro-v 0 Hydro-M star Random Lau 10 CSF N brightest galaxies in a halo N brightest galaxies in a cluster Lau 10 Munari 13 Velocity bias is consistent among (1) subhalos with v pk in N-body sims and (2) galaxies in CSF+AGN sims. D04 F06 N-body SAM NR CSF AGN ± A converging picture: b v = ± (stat) ± (sys)

18 Summary Velocity bias of galaxies can cause systematics in the measurements of growth of structure. When using galaxy P(k), 10% uncertainty in b v can leads to 5% systematic bias in P(k) at k=0.3 We present a converging picture of velocity bias from simulations: b v = 1.07±0.03, which is a combined effect of dynamical friction and tidal stripping.