Cluster density profiles and orbits of galaxies from dynamical analysis. Andrea Biviano, INAF/Oss.Astr.Trieste

Similar documents
MASS PROFILES OF GALAXY CLUSTERS from dynamics. Andrea Biviano INAF - Trieste

Clusters and groups of galaxies: internal structure and dynamics

The Caustic Technique An overview

RESULTS ABOUT GALAXIES FROM INTERNAL DYNAMICS OF GROUPS AND CLUSTERS

Galaxy evolution in clusters from the CLASH-VLT survey

Kinematic, lensing & X-ray mass estimates of a poor cluster

MAMPOSSt modeling of true & mock dwarf spheroidal galaxies

arxiv:astro-ph/ v1 2 Oct 2003

Joint mass and anisotropy modeling of the. Fornax Dwarf Spheroidal

SUPPLEMENTARY INFORMATION

The Los Cabos Lectures

arxiv: v2 [astro-ph.co] 22 Jul 2010

THE MASS PROFILE OF GALAXY CLUSTERS OUT TO 2r 200 Andrea Biviano. and Marisa Girardi

Mass modelling of dwarf spheroidals. Jorge Peñarrubia

2. What are the largest objects that could have formed so far? 3. How do the cosmological parameters influence structure formation?

Structure of Dark Matter Halos

Galaxy Clusters Across Cosmic Times: Opening Talk. Andrea Biviano, INAF-OATs

Testing the cold dark matter model with gravitational lensing

Milky Way s Mass and Stellar Halo Velocity Dispersion Profiles

cluster scaling relations and mass definitions

The Radial Distribution of Galactic Satellites. Jacqueline Chen

Rotation curves of spiral galaxies

Self-Interacting Dark Matter

DA(z) from Strong Lenses. Eiichiro Komatsu, Max-Planck-Institut für Astrophysik Inaugural MIAPP Workshop on Extragalactic Distance Scale May 26, 2014

The Formation and Evolution of Galaxy Clusters

arxiv:astro-ph/ v1 20 Apr 1998

Connecting observations to simulations arxiv: Joe Wolf (UC Irvine)

Fabrizio Nesti. LNGS, July 5 th w/ C.F. Martins, G. Gentile, P. Salucci. Università dell Aquila, Italy. The Dark Matter density. F.

Measuring galaxy environments with group finders: Methods & Consequences

Summary So Far! M87van der Maerl! NGC4342! van den Bosch! rotation velocity!

Milky Way s Anisotropy Profile with LAMOST/SDSS and Gaia

A tool to test galaxy formation theories. Joe Wolf (UC Irvine)

NMAGIC Made-to-Measure Modeling of Elliptical Galaxies NMAGIC - 2 M2M

Probing Dark Matter Halos with Satellite Kinematics & Weak Lensing

Firenze (JPO: 28/07/17) Small Scale Crisis for CDM: Fatal or a Baryon Physics Fix

The fine-scale structure of dark matter halos

Overview of Dynamical Modeling. Glenn van de Ven

The rotation of Galaxy Clusters

MOND and the Galaxies

GRAVITATIONAL FIELDS ON THE SCALE OF CLUSTERS OF GALAXIES

GALAXY ORBITS FOR GALAXY CLUSTERS IN THE SLOAN DIGITAL SKY SURVEY AND TWO DEGREE FIELD GALAXY REDSHIFT SURVEY

Constraints on dark matter annihilation cross section with the Fornax cluster

Infrared Mass-to-Light Profile Throughout the Infall Region of the Coma Cluster

Measuring the escape velocity and mass profiles of galaxy clusters beyond their virial radius

GALAXY MODELS WITH TANGENTIALLY ANISOTROPIC VELOCITY DISTRIBUTIONS

Phase Space Structure of Dark Matter Halos

The Stellar Initial Mass Function of Massive Galaxies

HYDRODYNAMICAL SIMULATIONS OF GALAXY CLUSTERS

For β = 0, we have from our previous equations: d ln ν d ln r + d ln v2 r

Constraining self-interacting dark matter with scaling laws of observed halo surface densities

The distribution and kinematics of early high-σ peaks in present-day haloes: implications for rare objects and old stellar populations

Insights into galaxy formation from dwarf galaxies

Course of Galaxies course organizer: Goeran Ostlin ESSAY. X-ray physics of Galaxy Clusters

Astro 358/Spring 2008 (49520) Galaxies and the Universe

astro-ph/ Aug 1995

Cluster Multi-Wavelength Studies: HSC/SC-WL + SL+ SZE + X-ray + Dynamics

Structure formation in the concordance cosmology

arxiv:astro-ph/ v2 7 Apr 2003

arxiv: v1 [astro-ph.co] 29 Sep 2011

Beyond the spherical dust collapse model

Collisionless Boltzmann Eq (Vlasov eq)" S+G sec 3.4! Collisionless Boltzmann Eq S&G 3.4!

Dark Matter and/or MOND in Giant Ellipticals

Princeton December 2009 The fine-scale structure of dark matter halos

arxiv: v2 [astro-ph.co] 20 May 2018

ASTR 610 Theory of Galaxy Formation Lecture 18: Disk Galaxies

Dark Matter Substructure and their associated Galaxies. Frank C. van den Bosch (MPIA)

Masses of Dwarf Satellites of the Milky Way

Astronomy. Astrophysics. A study of catalogued nearby galaxy clusters in the SDSS-DR4. II. Cluster substructure

What can M2M do for Milky Way Models?

AY202a Galaxies & Dynamics Lecture 7: Jeans Law, Virial Theorem Structure of E Galaxies

Distinguishing Between Warm and Cold Dark Matter

Dark matter from cosmological probes

Constraining Dark Matter Equation of State with CLASH Clusters

Connecting observations to simulations arxiv: Joe Wolf (UC Irvine)

The physical origin of stellar envelopes around globular clusters

The Cosmological Angular Momentum Problem of Low-Mass. Disk Galaxies

Dwarf Galaxies as Cosmological Probes

Kinematics of the Solar Neighborhood

Insights on galaxy evolution from the dark matter content of massive early-type galaxies Crescenzo Tortora

pseudo- evolution of halo mass and observable-mass relations Andrey Kravtsov The University of Chicago

Dynamics of Galaxy Clusters

Star formation activity and gas stripping in the Cluster Projected Phase-Space (CPPS)

arxiv: v1 [astro-ph.co] 14 Dec 2018

Fossil Galaxy Groups; Halo and all therein

The Current Status of Too Big To Fail problem! based on Warm Dark Matter cosmology

The Galaxy Dark Matter Connection. Frank C. van den Bosch (MPIA) Xiaohu Yang & Houjun Mo (UMass)

Milky Way disc dynamics and kinematics from mock data

SEMIANALYTICAL DARK MATTER HALOS AND THE JEANS EQUATION

arxiv: v1 [astro-ph.co] 1 Nov 2010

Components of Galaxies: Dark Matter

Dynamical friction, galaxy merging, and radial-orbit instability in MOND

The Galaxy Dark Matter Connection

MASS PROFILES OF X-RAY BRIGHT RELAXED GROUPS: METHODS AND SYSTEMATICS

Dark matter and galaxy formation

arxiv: v1 [astro-ph.co] 6 Sep 2012

Current status of the ΛCDM structure formation model. Simon White Max Planck Institut für Astrophysik

Connecting the small and large scales

CAULDRON: dynamics meets gravitational lensing. Matteo Barnabè

Dynamics of Galaxies: Practice. Frontiers in Numerical Gravitational Astrophysics July 3, 2008

4. Structure of Dark Matter halos. Hence the halo mass, virial radius, and virial velocity are related by

Transcription:

Cluster density profiles and orbits of galaxies from dynamical analysis Andrea Biviano, INAF/Oss.Astr.Trieste

Collaborators & papers: on the observational side: A.B. & M. Girardi 2003, P. Katgert, A.B. & A. Mazure 2004, A.B. & P. Katgert 2004, A.B. & P. Salucci 2006, A.B., G. Mamon,T. Ponman in prep., A.B., A. Diaferio, K. Rines in prep. on the numerical side: A.B., G. Murante, S. Borgani, A. Diaferio, K. Dolag, & M. Girardi 2006 & in prep.

Aims: 1. Constrain M(r) of galaxy systems - central cusp or core? - external slope? - influence of baryons? 2. How do galaxies move in their systems? - evidence of infall? - relaxation processes?

Tracers of the grav. potential: galaxies Observables: Ri radial distance of i-th galaxy from the cluster center Vi rest-frame l.o.s. velocity wrt the cluster <V> Because of poor sampling of single clusters and deviation from spherical symmetry... Combine many clusters using M200 Rn = R/r200 & Vn =(V-<V >)/ v200 M200 from σv (virial theorem) or Tx (scaling relations) when Ngal small

Why using the cluster galaxies to determine the total mass profile? - less direct than lensing and X-ray - sample mass profile to larger radii - IC gas not fully thermalized (?) (Rasia et al. 2004, Faltenbacher et al. 2005) - lensing inefficient for nearby clusters (Natarajan & Kneib 1997) and in any case, 3 is better than 1!

METHODS An example of YDS climbing class 1

M(<r): methods: 1. Jeans analysis (e.g. Binney & Tremaine 1987; see also Mamon & Boué 2007 - Works better in central cluster regions - Degeneracy mass profile - velocity anisotropies 2. Caustic method (Diaferio & Geller 1997; see also Diaferio 1999) - Works better in external cluster regions - Only mild dependence on velocity anisotropies

M(<r) from the Jeans analysis Assumes dynamical equilibrium of the system I(R) and p(r) (r), r(r), M(<r), through (r) or, more generally: fp(r,v) (r) + f(e,l2) Mass orbits degeneracy: given R,v the M(<r) solution depends on (r) ( (r) 1 - t2/ r2, velocity anisotropy profile) Our adopted solutions to this problem: analysis of the shape of the velocity distribution Use 2 independent tracers of the cluster potential

The Jeans equation r, clustercentric radial distance <vr2>, or r, radial component of velocity dispersion ν, number density of cluster galaxies β, velocity anisotropy:

M(<r) from the caustic method: Based on num.sims.: from caustic amplitude A(r) (r) (v-<v>)/ v (from Rines et al. 2003) through F(,,r) const...outside the center, indipendent of dynamical status of the cluster A R/r200

Galaxy orbits: methods: 1. Inverse Jeans analysis (Binney & Mamon 1982, see also Solanes & Salvador-Solé 1990) Given M(r), invert Jeans eq. (r) 2. Moments of velocity distribution (e.g. Merritt 1987, see also van der Marel et al. 2000) Determine 4th and 6th moments by Gauss-Hermite polynomial decomposition

SAMPLES An example of YDS climbing class 2

Samples (all at <z> 0): 1. ENACS (Katgert et al. 1996) 59 clusters, <σv>=699 km/s, 2700 gal.s within r200 2. CIRS (based on SDSS; Rines & Diaferio 2006) 65 clusters, <σv>=617 km/s, 3300 gal.s within r200 3. 2dFGRS (B. & Girardi 2003) 43 clusters, <σv>=490 km/s, 700 gal.s within r200 4. GEMS (Osmond & Ponman 2002) 31 groups with measured Tx, <σv>=370 km/s, 700 gal.s within r200

Stacked CIRS cluster in Rn,Vn space

Red (CM sequence) galaxies only Stacked CIRS cluster in Rn,Vn space

Blue galaxies only Stacked CIRS cluster in Rn,Vn space

MASS PROFILES cored or cuspy? external slope? An example of YDS climbing class 3

Fit models to the Vc(r) profiles The cuspy model of NFW, motivated by cosmological num. simulations with CDM: vs. the cored model of Burkert (1995), motivated by the problems of NFW on galactic scales (e.g., de Blok et al. 2003, Gentile et al. 2004):

ENACS NFW vs. Burkert i.e. cuspy vs. cored c = r200/rs = 5±1 rcore 0.1 r200

ENACS: (r) r-2.4±0.4 at r=r200 Fitting models: NFW Burkert 95 Isothermal gives poor fit

From Rines & Diaferio (2006) CIRS: individual cluster mass profiles Fitting models: Solid lines: NFW c=3,5,10 Short dashed: Hernquist Long dashed: SIS

2dFGRS: combine the two methods: The caustic M(r) nicely continue the M(r) found with the Jeans solution, (r) ~ r-3 at large r

MASS PROFILES the baryonic components An example of YDS climbing class 4

ENACS: M(<r) subdivided into its components diffuse DM subhalo DM baryons IC gas galaxies

c for DM only > c for total M(r) (but only slightly) total mass diffuse DM subhalo DM baryons IC gas galaxies

c for DM only > c for total M(r) (but only slightly)

GEMS anisotropy c for DM only > c for total M(r) (but only slightly) concentration

MASS PROFILES c=c(m) An example of YDS climbing class 5

NFW c=c(m): observations vs. theoretical predictions (ΛCDM; Dolag et al. 2004) RED: c for DM only BLUE: c for TOTAL M(r)

GALAXY ORBITS i.e. velocity anisotropy profiles β'(r) σr(r)/σt(r) An example of YDS climbing class 5.3

Must distinguish red (early-type) from blue (late-type) galaxies because they have different Rn,Vn distributions (hence different orbits may be expected given the same mass profile) CIRS

CIRS, caustic M(r) from Rines & Diaferio (2006) Red galaxies move on isotropic orbits

CIRS vs. ENACS Isotropic orbits for red gal.s: samples

CIRS: Jeans vs. Gauss-Hermite Isotropic orbits for red gal.s: methods

CIRS: red vs. blue galaxies Blue galaxies move on more radial orbits

CIRS vs. ENACS Radial orbits for blue gal.s: samples

ENACS Substructures (groups in clusters): tangential anisotropy

Gal.s in small σv groups: energy dissipation? 7 out of 31 groups have very small σv given their Tx: their galaxies move on slightly tangential orbits anisotropy GEMS: concentration

SHOULD WE TRUST THESE RESULTS? cmp with numerical simulations: masses and mass profiles An example of YDS climbing class 5.6

compare to clusters extracted from cosmological simulations (from Borgani et al. 04) and projected Virial mass estimates unbiased for Npart 60

compare to clusters extracted from cosmological simulations and projected Virial mass estimates unbiased for Npart 60 For smaller Ngal select 'old' (red) galaxies Global dynamical estimates for clusters are OK: M200can be used for scaling vel.s and radii (unless Ngal very small: groups; use Tx)

Apply the Jeans method to projected data: M(r) (β(r) from projected velocity distribution moments) Stacked cluster from Borgani's simulations, 4000 objs

Single cluster from GIF simulations, 9 projections From Diaferio (1999) Determine M(r) with the caustic method: Mpc/h 8 6 4 2 0

SHOULD WE TRUST THESE RESULTS? cmp with numerical simulations: velocity anisotropy profiles (orbits) An example of YDS climbing class 5.9

Stacked cluster from Borgani et al.'s simulations: true velocity anisotropy profile cmpd to profile obtained from the analysis of the velocity moments of projected data (Gauss-Hermite)

Stacked cluster from Borgani et al.'s simulations: true velocity anisotropy profile cmpd to profile obtained from Jeans analysis of projected data, given M(r) Overestimate probably due to unidentified interlopers in (R,v) space

True velocity anisotropy profile cmpd to profiles obtained from Jeans analysis, using true M(r), isotropic M(r) Jeans solution, and anisotropic M(r) Jeans solution (as obtained from Gauss-Hermite analysis) Accurate β'(r) determination requires accurate M(r) determination

ORBITS OF GALAXIES IN CLUSTERS: EVOLUTION

Compare ENACS vs. CNOC nearby distant Number density profiles for early- (empty symbols) and late- (filled symbols) cluster galaxies

Compare ENACS vs. CNOC nearby distant σp(r) profiles for early- (empty symbols) and late- (filled symbols) cluster galaxies

Early-type galaxies at z 0 & z 0.3: isotropic orbits (Katgert, B. & Mazure 04; van der Marel et al. 00) Late-type galaxies at z 0: radial orbits (B. & Katgert 04) No evolution of (R,v) distributions of early- and late-type galaxies from z 0 to z 0.3 (Carlberg et al. 97 vs. B. & Katgert 04) late-type galaxies at z 0.3 must also be on radial orbits like late-type galaxies at z 0 The late-type -galaxy fraction increases with z, hence more cl. galaxies are on radial orbits at higher z the infall rate increases with z (in agreement with Ellingson et al. 2001)

CONCLUSIONS An example of YDS climbing class 6

Density profiles of clusters are as predicted by ΛCDM models; if cored, core 0.1 r200 ~ cd size ( DM particles cross-section) DM more concentrated than baryons ( effectiveness of dyn. friction & adiabatic contraction) Groups: higher concentration than predicted by ΛCDM? dissipation processes at work? (...TBC: need better statistics) Red (early-type) galaxies move on isotropic orbits, Blue (late-type) galaxies move on slightly radial orbits Substructures move on tangential orbits ( cluster accretion history, galaxy evolution in clusters)

Density profiles of clusters are as predicted by ΛCDM models; if cored, core 0.1 r200 ~ cd size ( DM particles cross-section) DM more concentrated than baryons ( effectiveness of dyn. friction & adiabatic contraction) Groups: higher concentration than predicted by ΛCDM? dissipation processes at work? (...TBC: need better statistics) Red (early-type) galaxies move on isotropic orbits, Blue (late-type) galaxies move on slightly radial orbits Substructures move on tangential orbits ( cluster accretion history, galaxy evolution in clusters)

Density profiles of clusters are as predicted by ΛCDM models; if cored, core 0.1 r200 ~ cd size ( DM particles cross-section) DM more concentrated than baryons ( effectiveness of dyn. friction & adiabatic contraction) Groups: higher concentration than predicted by ΛCDM? dissipation processes at work? (...TBC: need better statistics) Red (early-type) galaxies move on isotropic orbits, Blue (late-type) galaxies move on slightly radial orbits Substructures move on tangential orbits ( cluster accretion history, galaxy evolution in clusters)

Density profiles of clusters are as predicted by ΛCDM models; if cored, core 0.1 r200 ~ cd size ( DM particles cross-section) DM more concentrated than baryons ( effectiveness of dyn. friction & adiabatic contraction) Groups: higher concentration than predicted by ΛCDM? dissipation processes at work? (...TBC: need better statistics) Red (early-type) galaxies move on isotropic orbits, Blue (late-type) galaxies move on slightly radial orbits Substructures move on tangential orbits ( cluster accretion history, galaxy evolution in clusters)

The best fitting Burkert core-radius is small, 0.1 r200 ~ size of central cd DM scattering cross section <2 cm2 g-1 (By comparison with simulation res. of Meneghetti et al. 2001) Much smaller than the 5 cm2g-1 needed to fit dwarf galaxy mass density prof., Davé et al. (2000) DM is more concentrated than the total matter Dynamical friction mechanism ineffective in transferring galaxy energy to DM in clusters or counteracted by adiabatic contraction (e.g. Zappacosta et al. 2006)

Future work Num.simulations: investigate physics of evolution of orbits of galaxies in clusters and find optimal algorithm for M(r), β(r) (ongoing collaboration with Borgani, Dolag, Mamon, Murante et al.) More data: Improve current constraints on cluster M(r) and (r) by combining existing data-bases and using new ones (e.g.: W I N G S, see A. Cava's poster, poster ongoing collaboration with Bettoni, Cava, D'Onofrio, Fasano, Moles, Poggianti, Ramella, Varela) Higher z: Evolution of M(r) and β(r), extend the analysis to higher-z cluster samples (e.g.: I C B S, possible collaboration with Dressler, Poggianti et al.)

Thank you for your attention! An example of YDS climbing class 7 (...my favorite!)