Cargèse - August 2006 Semi-analytics and mock catalogues as tools to observe ideas I.Semi-analytic modelling of galaxy formation The long way from first

Cargèse - August 2006

Semi-analytics and mock catalogues Semi-analytics and mock catalogues as tools to observe ideasas tools to observe ideas

I. Semi-analytic modelling of galaxy formation The long way from first principles to the distribution of galaxy properties

II. Mocking the Universe Construction, limitations and examples of mock catalogues

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Semi-analytic modelling Semi-analytic modelling of galaxy formation of galaxy formation

Jérémy Blaizot (MPA)



“Y a des progrès à faire du côté de la gastrophysique” … F. R. Bouchet


Colless et al., 2001

To what extent are galaxies tracers of DM

Physical “sampling” (bias) + observational

selection

Large-scale surveysLarge-scale surveys


From low to high redshiftsFrom low to high redshifts

Driver et al. 1998

SAMs and mocks provide a means to connect populations of galaxies selected in different

ways at different redshifts

(e.g. LBGs/BXs/etc. from Steidel’s group)

Galaxies @ z = 0.4

Galaxies @ z = 2.6


The ISO-HDF Project (Mann et al.)

Sources 15m

Sources 6.7m

ISO

HST

SAMs and mocks help establish the connection between populations of galaxies

selected at different wavelengths

Observations at different wavelengthsObservations at different wavelengths


On top of these motivations, there is the increasing need to produce “realistic” catalogues that can be used:

- to prepare forthcoming observations

- to validate analysis techniques used on real obs.

- to check/understand biases & uncertainties (e.g. cosmic variance)

Last but not least … Last but not least …


Structure formationStructure formation

Dark matter hierarchical structure formation

Given initial conditions and a cosmological model, we know how to describe the formation of dark matter structures with N-body simulations.


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Structure formation : N-body simulationsStructure formation : N-body simulations


It all happens in haloes…It all happens in haloes…

Semi-analytics neglect the impact of baryons

on the formation of large scale structures, and can thus be described

a posteriori within the hierarchy of haloes and

their evolution. The hybrid approach exploits our best way to describe structure

formation : N-body simulations.


Cooling (metallicity, structure, …)

Star formation (threshold, efficiency, IMF, …)

Dust (formation, distribution, heating & cooling, …)

Winds (IGM heating, enrichment, SN feedback, etc…)

AGNs (BH growth, feedback, …)

Galaxy interactions (morphological transformations, starbursts, intracluster stars, …

Stellar evolution (spectro-photometric evolution, yields, SN I/II rates,…)

Galaxy formation &

evolution

Galaxy formation : relevant processesGalaxy formation : relevant processes


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I. Implementation of the “hybrid” approach

II. Limitations of SAMs

III. Example : Brightest cluster galaxies


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z=0

z=3

z=1

From particles to « haloes »

Halo identification (FOF) and characterisation (Mass, Spin, Energies, etc.)

From particles to haloesFrom particles to haloes


SUBFIND (Springel et al. 2001)

ADAPTAHOP (Aubert et al. 2004)

Identification of sub-structures from the density field (only)

(Sub-)Halo finders …(Sub-)Halo finders …


z=0

z=3

z=1

From particles to « haloes »

From density evolution to merger

trees

Halo identification (FOF) and characterisation (Mass, Spin, Energies, etc.)

Construction of a full merger tree (mergers, accretion, fragmentation, evaporation)

From particles to halo merger treesFrom particles to halo merger trees


Example of a Cluster’s treeExample of a Cluster’s tree

Tidal stipping


Spin ()Hot gas (Tvir)

Galaxy mergers

coolingDisc formation

Star formation

FeedbackMetal enrichment

(ICM & IGM)

Stellar evolutionMetal enrichment (ISM)

+ model of simple stellar population evolution (w/ dust)

Semi-analyticsSemi-analytics


Cooling (source term…)Cooling (source term…)

White & Rees (1978)

Binney (1977), Silk (1977)

Assume hydrostatic equilibrium (+ isothermal) : temperature and density profile.

Note : cooling rates are sensitive to the heavy elements content of the gas (Z).

Cooling time (function of radius) :

Mass of gas that actually cools :

Free-fall radius


Cooling (source term…)Cooling (source term…)

“cold accretion” (rapid cooling)

Quasi-static contraction (inefficient cooling)

Transition at ~ 1012Msun (with some redshift dependency)

Kravtsov et al.


Star formation rate :(highl redshifts ?)

Star formation & feedbacksStar formation & feedbacks

Kennicutt (1998)

gas

SFR

Supernovae feedback :(highly uncertain)

or not …

Metal enrichment : (hyper-highly uncertain)

Fixed yield ? Instantaneous recycling ? Instantaneous mixing ?


Bulge formation

Fraction of progenitor disk mass tranfered to descendent’s bulge.

50 %

100 %

0 %

Major mergers

Minor mergers

m2 / m1 10

Disrupted disk (m1 = m2)

No bulge (m1 >> m2)

Galaxy mergers - galaxy morphologiesGalaxy mergers - galaxy morphologiesGalaxies spiral down haloes’ potential wells due to

dynamical friction. When they reach the center they merge with the central galaxy.


Spectral energy distributionsSpectral energy distributionsFinal SED is the sum of SEDs of stars formed all along the hierarchical history …

- stellar evolutionary tracks (Padova tracks, Genova, -enhancement ? )

- stellar spectra library

- IMF … (Chabrier, Kennicutt, Salpeter …)

- Extinction/emission by dust.


spirals ellipticals

Gas+starsSFR

Stellar mass

THE result …THE result …


THE result …THE result …


Frequently asked questionsFrequently asked questions

- Do you “resolve” galaxies ?

NO ! Galaxies in a SAM are “vectors” : {Mstar, etc, …}

- How many parameters do you fit ?

I wish I knew… Lucky we don’t “fit” …

- What do you get that you didn’t put in by hand ?

A quantitative estimate of the coupled evolution of a set of processes (each “put by hand”) within a complex system of boundary conditions (merger trees).


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John Helly (Durham : http://www.virgo.dur.ac.uk/)

Semi-analytic galaxies D.M. density

SAM Cinema … SAM Cinema …


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Chosing a simulation Chosing a simulation

Trade-off between :

- Mass resolution (ability to describe history + faint objects)

- Volume (ability to describe rare objects)


galics 1

galics 3

2dF

Halo mass resolution

“Galics 1” : 1.6 1011Msun

“Galics 3” : 2.8 109Msun

Effects of mass resolution (1/3)Effects of mass resolution (1/3)• completeness limit galaxies in small mass haloes are missing.


1010 MO

1011 MO

1012 MO

1013 MO

Effects of mass resolution (2/3)Effects of mass resolution (2/3)• completeness limit galaxies in small mass haloes are missing.• redshift limit beyond zlim, there are no resolved haloes.


• history resolution properties of new galaxies are not realistic

galics 1Mh = 2 1011 Msun

galics 3Mh = 3 109 Msun

• redshift limit beyond zlim, there are no resolved haloes.

• completeness limit galaxies in small mass haloes are missing.

Effects of mass resolution (3/3)Effects of mass resolution (3/3)


Other limitations … Other limitations …

Each step of the post-processing involve approximations that do not disapear even if the results fit the observations !

- halo finder : N-body describes exactly the (non-linear) evolution of a density field … haloes are not so exact…

- halo merger trees : following sub-structures is a delicate business …

- galaxy mergers : largely unknown … (both when & how)

- metals : production, transport …

- SEDs : if you don’t believe in BC03 or Chabrier’s IMF …


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Brightest Cluster Galaxies (BCGs)Brightest Cluster Galaxies (BCGs)

Brightest (and central) galaxies of the most massive haloes of the Universe (typically Mhalo ~ 1015 Msun)

Selection of clusters (e.g. with LX), so far possible up to z ~ 1

BCGs are the galaxies with the richest merger trees



De Lucia & Blaizot (2006)



De Lucia & Blaizot (2006)



: 2 x 2 Mpc (comoving)



Mass growth ~ 3 since z=1 (along the “main branch”)

Infered mass growth ~ 3 since z=1 (“total”)

High-z BCGs are do not end up in local BCGs…


The monolithic approximation (isolated evolution or “one-branch tree”) is wrong in general and should not be used to try to assess evolutionary links between galaxy populations observed at different redshifts.


The proper way to go is to reproduce observational selections on the model galaxies, using mock catalogues, and then go back to the model to understand the (hierarchical) links between galaxies selected in different ways.


SAMs & mock cataloguesSAMs & mock cataloguesfor interpreting observationsfor interpreting observations

Jérémy Blaizot (MPA)




Colless et al., 2001

To what extent are galaxies tracers of DM

Physical “sampling” (bias) + observational

selection

Selections … Selections …


The ISO-HDF Project (Mann et al.)

Sources 15m

Sources 6.7m

ISO

HST

Selections, selections … Selections, selections … SAMs + Mocks help establish the connection between populations of galaxies selected at

different wavelengths


Selections, selections, hierarchical evolution …Selections, selections, hierarchical evolution …

SAMs and mocks provide a means to connect (statistically) populations of galaxies selected in different ways at different

redshifts

(e.g. LBGs/BXs/etc. from Steidel’s group)

Galaxies @ z = 0.4

Galaxies @ z = 2.6


ObservationsObservationsTheoretical FrameworkTheoretical Framework

General frameworkGeneral framework

Physical model

(“ingredients” & “Recipes”)

Hybrid implementation Some comparison to obs.

Surveys

Galaxy samples @ diff. z &

Mock Catalogues


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I. Construction of mock catalogues

II. Limitations of mock catalogues

III. Example 2 : Lyman Break Galaxies

IV. Just do it …


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IV. Just do it …


Inputs for mock cataloguesInputs for mock catalogues

Series of napshots at zsnap = zi (i = 1, …,

N)

- Observer-frame (zsnap) absolute magnitudes and their derivative :

- positions / velocities- size(s), inclination- IDs


Tiling boxes … basicsTiling boxes … basics


dec.

r.a.

Tiling boxes … replicationsTiling boxes … replications


“Random tiling”

dec.

r.a.

Tiling boxes … random tilingTiling boxes … random tiling

Supresses replication effects … and some of the signal (see later)


18 < r < 1918 < r < 19

21 < r < 2221 < r < 22

20 < r < 2120 < r < 21

19 < r < 2019 < r < 20

Example 1 : mock SDSS stripeExample 1 : mock SDSS stripe


3 arcmin

6 arcminJohnson V filter HDF

Example 2 : mock V-band deep fieldExample 2 : mock V-band deep field


SkyMaker (E. Bertin)SkyMaker (E. Bertin)


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IV. Just do it …


Correlation functionsCorrelation functions

Excess probability of finding a pair of galaxies at a given separation, relative to a random distribution.

Data-Data Random-Random

Field-to-field variance (in counts) ~ average of over field-size


Random pairs

Negative bias typically peaking around r0, with

amplitude :

Random tiling biasRandom tiling bias


Finite Finite VoluVolumeme

R.T. biasR.T. bias

Random tiling biasRandom tiling bias100 Mpc/h

12 Mpc/h

Analytic estimate

R.T. bias present around r0, but well understood.

Finite volume effects (integral constraint) comes in at larger scales…


100 Mpc/h

20 Mpc/h

Finite-volume effects & correlation functionFinite-volume effects & correlation function

A simulation does not contain fluctuations (clustering) on scales larger

than Lbox


Finite-volume effect & cosmic varianceFinite-volume effect & cosmic variance

Simulation volume should be >> light-cone volume …


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IV. Just do it …


(e.g.) Adelberger et al. (1998) :

LBG selection (at z=3)LBG selection (at z=3)

Blaizot et al. (2004)

Pure photometric selection : good test for the model and mock-catalogue methodology


1.1 Gyr

1.3 Gyr

0 Gyr

galics 3

LBG counts and cosmic varianceLBG counts and cosmic variance

Clustering of LBGs dominates cosmic variance up to (at least) 1 deg.


C’est « ca va » !

Steidel’s team

30% of LBGs’ intense SF is triggered by

mergers

LBGs : physical propertiesLBGs : physical properties


The Epoch of Galaxy Formation, Baugh et al. 1998

zz

LBGs

Link to local galaxies (1/2)Link to local galaxies (1/2)


E + S0 with LBG E + S0 with LBG prog. at z=3prog. at z=3

Other E + S0

Sp with LBG prog. at z=3

LBGs at z=3

z = 3 z = 0

77% of z=3 LBGs end up in E or S0 at z = 035% of local E or S0 have a LBG progenitor at z = 3

Link to local galaxies (2/2)Link to local galaxies (2/2)


IV. Just do it …

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Online stuff … Online stuff …

box halo galaxy

cone

Cosmological quantities at

each snapshot(e.g. redshift,

number of halos, mass of

stars)

Physical props.

Hierarchical links,

Spatial information.

Physical props.

Hierarchical links,

Rest-frame magnitudes.

Spatial information,

Apparent magnitudes.

Observer-framespectra

Rest-framespectra

Mock Images


Online stuff …Online stuff …

http://www.g-vo.org/Millennium/

(Gerard Lemson)

Documents

Cargèse - August 2006 Semi-analytics and mock catalogues as tools to observe ideas I.Semi-analytic modelling of galaxy formation The long way from first