(Toward) A Solution to the Hydrostatic Mass Bias Problem in Galaxy Clusters. Eiichiro Komatsu (MPA) UTAP Seminar, December 22, 2014

Transcription

1 (Toward) A Solution to the Hydrostatic Mass Bias Problem in Galaxy Clusters Eiichiro Komatsu (MPA) UTAP Seminar, December 22, 2014

2 References Shi & EK, MNRAS, 442, 512 (2014) Shi, EK, Nelson & Nagai, arxiv: Xun Shi (MPA) Kaylea Nelson (Yale)

3 Motivation We wish to determine the mass of galaxy clusters accurately

4 Where is a galaxy cluster? Subaru image of RXJ (Medezinski et al. 2009)

5 Where is a galaxy cluster? Subaru image of RXJ (Medezinski et al. 2009)

6 Subaru image of RXJ (Medezinski et al. 2009)

7 Hubble image of RXJ (Bradac et al. 2008)

8 Chandra X-ray image of RXJ (Johnson et al. 2012)

9 Image of the Sunyaev-Zel dovich effect at 150 GHz [Nobeyama Radio Observatory] (Komatsu et al. 2001) Chandra X-ray image of RXJ (Johnson et al. 2012)

10 I X = Z Multi-wavelength Data dl n 2 e (T X ) I SZ = g T k B m e c 2 Z dl n e T e Optical: galaxies velocity dispersion gravitational lensing X-ray: hot gas ( K) spectroscopic TX Intensity ~ ne 2 L SZ [microwave]: hot gas ( K) electron pressure Intensity ~ netel

11 Galaxy Cluster Counts We count galaxy clusters over a certain region in the sky [with the solid angle Ωobs] Our ability to detect clusters is limited by noise [limiting flux, Flim] For a comoving number density of clusters per unit mass, dn/dm, the observed number count is N = obs Z 1 0 dz d2 V dzd Z 1 F lim (z) df dn dm dm df

12 DE vs Galaxy Clusters Counting galaxy clusters provides information on dark energy by Providing the comoving volume element which depends on da(z) and H(z) Providing the amplitude of matter fluctuations as a function of redshifts, σ8(z)

13 Comoving Volume, V(<z), over 1000 deg 2 [Gpc 3 /h 3 ] V (< z)= Z 1000 deg 2 d redshift_volume_1000_w1.txt u 1:($2*1e-9) redshift_volume_1000_w09.txt u 1:($2*1e-9) redshift_volume_1000_w11.txt u 1:($2*1e-9) Z z Redshift, z dz 0 d 2 V dz 0 d w= 1.1 w= 0.9 m =0.3 de =0.7

14 Mass Function, dn/dm The comoving number density per unit mass range, dn/dm, is exponentially sensitive to the amplitude of matter fluctuations, σ8, for high-mass, rare objects By high-mass objects, we mean high peaks, satisfying 1.68/σ(M) > 1

15 Mass Function, dn/dm The comoving number density per unit mass range, dn/dm, is exponentially sensitive to the amplitude of matter fluctuations, σ8, for high-mass, rare objects By high-mass objects, we mean high peaks, satisfying 1.68/σ(M) > 1

16 Comoving Number Density of DM Halos [h 3 /Mpc 3 ] (Tinker et al. 2008) e-06 1e-08 1e-10 1e-12 1e-14 b =0.05, cdm =0.25 de =0.7, w = 1 H 0 = 70 km/s/mpc 1e+14 1e+15 Dark Matter Halo Mass [Msun/h] Mh_dndlnMh_z0_s807.txt Mh_dndlnMh_z05_s807.txt Mh_dndlnMh_z1_s807.txt Mh_dndlnMh_z0_s808.txt Mh_dndlnMh_z05_s808.txt Mh_dndlnMh_z1_s808.txt z=0 z=0.5 z=1 dn/dm falls off exponentially in the cluster-mass range [M>10 14 Msun/h], and is very sensitive to the value of σ 8 and redshift This can be understood by the exponential dependence on 1.68/σ(M,z) σ8=0.8 σ8=0.7 σ8=0.8 σ8=0.7 σ8=0.8 σ8=0.7

17 Cumulative mass function from X-ray cluster samples Chandra Cosmology Project Vikhlinin et al. (2009)

18 Cumulative mass function from X-ray cluster samples Chandra Cosmology Project Vikhlinin et al. (2009)

19 The Challenge N = obs Z 1 0 dz d2 V dzd Z 1 F lim (z) df dn dm dm df Cluster masses are not directly observable The observables F include Number of cluster member galaxies [optical] Velocity dispersion [optical] Strong- and weak-lensing masses [optical] Mis-estimation of the masses from the observables severely compromises the statistical power of galaxy clusters as a DE probe X-ray intensity [X-ray] X-ray spectroscopic temperature [X-ray] SZ intensity [microwave]

20 HSE: the leading method Currently, most of the mass cluster estimations rely on the X-ray data and the assumption of hydrostatic equilibrium [HSE] The measured X-ray intensity is proportional to ne 2 dl, which can be converted into a radial profile of electron density, ne(r), assuming spherical symmetry The spectroscopic data give a radial electron temperature profile, Te(r) These measurements give an estimate of the electron pressure profile, Pe(r)=ne(r)kBTe(r)

21 HSE: the leading method Recently, more SZ measurements, which are proportional to nekbte dl, are used to directly obtain an estimate of the electron pressure profile

22 HSE: the leading method In the usual HSE assumption, the total gas pressure [including contributions from ions and electrons] gradient balances against gravity ngas = nion+ne = [(3+5X)/(2+2X)]ne = 1.93ne Assuming Tion=Te [which is not always satisfied!] [X=0.75 is the hydrogen mass abundance] Pgas(r) = 1.93Pe(r) Then, HSE 1 gas gas = GM(< r) r 2 gives an estimate of the total mass of a cluster, M

23 Limitation of HSE The HSE equation 1 gas gas = GM(< r) r 2 only includes thermal pressure; however, not all kinetic energy of in-falling gas is thermalised There is evidence that there is significant nonthermal pressure support coming from bulk motion of gas (e.g., turbulence) Therefore, the correct equation to use would be 1 gas th (r)+p th (r)] = GM(< r) r 2 Not including Pnon-th leads to underestimation of the cluster mass!

24 Planck SZ Cluster Count, N(z) Planck CMB+SZ best fit with MHSE/Mtrue=0.6 40% HSE mass bias?! Planck CMB prediction with MHSE/Mtrue=0.8 Planck Collaboration XX, arxiv: v2

25 Shaw, Nagai, Bhattacharya & Lau (2010) Simulations by Shaw et al. show that the non-thermal pressure [by bulk motion of gas] divided by the total pressure increases toward large radii. But why?

26 Battaglia, Bond, Pfrommer & Sievers (2012) P kin / P th 1.0 AGN feedback, z = x M O < M 200 < 1.7 x M O 1.7 x M O < M 200 < 2.7 x M O 2.7 x M O < M 200 < 4.2 x M O 4.2 x M O < M 200 < 6.5 x M O 6.5 x M O < M 200 < 1.01 x M O 1.01 x M O < M 200 < 1.57 x M O Shaw et al Trac et al R 500 R vir r / R 200 Battaglia et al. s simulations show that the ratio increases for larger masses, and

27 Battaglia, Bond, Pfrommer & Sievers (2012) P kin / P th 1.0 AGN feedback, 1.7 x M O < M 200 < 2.7 x M O z = 0 z = 0.3 z = 0.5 z = 0.7 z = 1.0 z = 1.5 Shaw et al. 2010, z = 0 Shaw et al. 2010, z = R 500 R vir r / R 200 increases for larger redshifts. But why?

28 Part I: Analytical Model Shi & Komatsu (2014) Xun Shi (MPA)

29 Analytical Model for Non- Thermal Pressure Basic idea 1: non-thermal motion of gas in clusters is sourced by the mass growth of clusters [via mergers and mass accretion] with efficiency η Basic idea 2: induced non-thermal motion decays and thermalises in a dynamical time scale Putting these ideas into a differential equation: Shi & Komatsu (2014) [σ 2 =P/ρgas]

30 Finding the decay time, t d Think of non-thermal motion as turbulence Turbulence consists of eddies with different sizes

31 Finding the decay time, t d Largest eddies carry the largest energy Large eddies are unstable. They break up into smaller eddies, and transfer energy from large-scales to smallscales

32 Finding the decay time, t d Assumption: the size of the largest eddies at a radius r from the centre of a cluster is proportional to r Typical peculiar velocity of turbulence is r GM(< r) v(r) =r (r) = r Breaking up of eddies occurs at the time scale of t d 2 (r) t dynamical We thus write: t d t dynamical 2

33 Shi & Komatsu (2014) Dynamical Time Dynamical time increases toward large radii. Non-thermal motion decays into heat faster in the inner region

34 Source term Define t growth 2 tot d 2 tot dt 1

35 Shi & Komatsu (2014) Growth time increases toward lower redshifts and smaller masses. Non-thermal motion is injected more efficiently at high redshifts and for large-mass halos Growth Time

36 Shi & Komatsu (2014) Non-thermal Fraction, fnth=pnth/(pth+pnth) approximate fit to hydro simulations Non-thermal fraction increases with radii because of slower turbulence decay in the outskirts η = turbulence injection efficiency β = [turbulence decay time] / 2tdyn

37 Shi & Komatsu (2014) Non-thermal Fraction, fnth=pnth/(pth+pnth) Non-thermal fraction increases with redshifts because of faster mass growth in early times η = turbulence injection efficiency β = [turbulence decay time] / stdyn

38 With P non-thermal computed We can now predict the X-ray and SZ observables, by subtracting Pnon-thermal from Ptotal, which is fixed by the total mass We can then predict what the bias in the mass estimation if hydrostatic equilibrium with thermal pressure is used

39 Shi & Komatsu (2014) Pressure [ev/cm 3 ] Excellent match with observations! [black line versus green dashed] total pressure observed thermal predicted thermal

40 [Hydrostatic Mass] / [True Mass] Shi & Komatsu (2014) Typically ~10% mass bias for massive clusters detected by Planck; seems difficult to get anywhere close to ~40% bias

41 Part II: Comparison to Simulation Shi, Komatsu, Nelson & Nagai (2014) Kaylea Nelson (Yale) Xun Shi (MPA)

42 Cluster-by-cluster Comparison So far, the results look promising We have shown that the simple analytical model can reproduce simulations and observations on average But, can we reproduce them on a cluster-by-cluster basis?

43 Approach We solve Using the measured σtot 2 (t) from a simulation on a particular cluster, and predict the non-thermal pressure. We them compare the prediction with the measured non-thermal pressure from the same cluster

44 Nelson et al. (2014) Omega500 Simulation A sample of 62 clusters simulated in a cosmological N-body+hydrodynamics simulation Using the ART code of Kravtsov and Nagai 500/h Mpc volume grids with refinements up to the factor of 2 8 Maximum spatial resolution of 3.8/h kpc

45 Nelson et al. (2014) Omega500 Simulation

46 Shi, Komatsu, Nelson & Nagai (2014) Mass growth history

47 Shi, Komatsu, Nelson & Nagai (2014) σtot 2 growth history

48 Shi, Komatsu, Nelson & Nagai (2014) Non-thermal Fraction, fnth=pnth/(pth+pnth) Mean and Scatter β=1 η=0.7 Simulation results (both the mean and scatter) are reproduced very well!

49 Shi, Komatsu, Nelson & Nagai (2014) Cluster-by-cluster β=1 η=0.7 The analytical model can predict the nonthermal fraction in each cluster

50

51 Dependence on the mass accretion history Separate the samples into fast accretors and slow accretors by using a mass accretion proxy: 200m log[m(z = 0)/M (z =0.5)] log[a(z = 0)/a(z =0.5)]

52 Shi, Komatsu, Nelson & Nagai (2014) Dependence on the mass accretion history It is clear that fast accretors have larger non-thermal pressure, because the injection of non-thermal motion is more efficient while the dissipation time is the same 200m log[m(z = 0)/M (z =0.5)] log[a(z = 0)/a(z =0.5)]

53 Shi, Komatsu, Nelson & Nagai (2014) The model still works for fast accretors The model is able to reproduce the nonthermal fraction on a cluster-by-cluster basis for fast accretors The scatter is somewhat larger

54 Toward A Solution to the Hydrostatic Mass Bias Problem A Proposal

55 All we need is the mass accretion history of a halo How do we estimate the source term (i.e., the second term on the right hand side)? The answer may be in the density profile in itself!

56 NFW fits both Ludlow et al. (2013) Consider the density profile of a halo, ρ(r) You can convert this into the mass, M, as a function of the mean density within a certain radius, <ρ> Consider the mass accretion history, M(z) You can convert this into the mass, M, as a function of the critical density of the universe at the same redshift, ρcrit(z) Remarkably, they agree!

57 NFW fits both Ludlow et al. (2013) You can convert ρ(r) into the mass, M, as a function of the mean density within a certain radius, <ρ>

58 NFW fits both Ludlow et al. (2013) You can show M(z) as a function of the critical density of the universe at the same redshift, ρcrit(z)

59 Concentration Parameter Relation Ludlow et al. (2013) While the NFW profile fits both, their respective concentration parameters are different There is a [cosmologydependent] relationship between them

60 A Proposal Take the X-ray or SZ data Compute the mass density profile using the hydrostatic equilibrium 1 gas gas = GM(< r) r 2 Compute the mass accretion history from the inferred density profile Compute the non-thermal pressure profile from the mass accretion history

61 A Proposal Compute the non-thermal pressure profile from the mass accretion history Re-compute the mass density profile using the observed thermal pressure and the inferred nonthermal pressure 1 gas th (r)+p th (r)] Re-compute the mass accretion history Re-compute the non-thermal pressure, and repeat = GM(< r) r 2

62 Summary A simple analytical model works! In agreement with simulations and the Planck data on average In agreement with simulations on a cluster-bycluster basis We have a physically-motivated approach to correcting for the hydrostatic mass bias It seems that the only missing piece at the moment is the cosmology dependence of the concentration parameter relationship