1. Galaxy merging in the Millennium simulation Serena Bertone - UC Santa Cruz Chris Conselice - U. Nottingham arXiv:0904.2365 MNRAS, in press Cosmoclub, April 27 th, 2009

2. Overview The Millennium simulation Techniques to identify mergers: –in observations –in simulations Results: –merger fraction evolution –merger rate evolution –dependence on stellar mass –dependence on time-scale for merging –problems in models and observations

3. The Millennium simulation Still the largest N-body simulation ever run (Springel et al 2005): 500 Mpc/h box >10 billion DM particles >20 million galaxies with M DM >10 10 M  4 public galaxy catalogues: Croton et al 2005 Bower et al 2006 De Lucia & Blaizot 2007 Bertone, De Lucia & Thomas 2007

4. Cosmological model IC from CMB: WMAP(1) DM evolution Millennium Springel et al. 2005 N-body simulation FOF group finder Halo merger trees Evolution of galaxies SA galaxy formation model Galaxy catalogues: observed galaxy properties SB, De Lucia & Thomas 2007 stellar pop synt. models dust extinction etc Galaxy Formation in a Nutshell

5. Galaxy Formation Physics Cold Gas (ISM) Hot Gas (ICM) Winds (SN feedback) Stars recycling star formation cooling ejection re-incorporation shock-heating Black Hole accretion

6. Bertone et al 2007 SA model: same physical model as De Lucia & Blaizot 2007 New: SN winds modelled as astrophysical blastwaves in a cosmological context (Ostriker & McKee 1988) two-phase model for the long-term evolution of winds –adiabatic, pressure-driven expansion (Hoopes et al 2004, Strickland & Stevens 2000) –momentum-driven snowplough (Aguirre et al 2001, Theuns et al 2001) Physics of Winds hot bubble  cold bubble thin cold shell 

7. How do galaxies accrete mass? Merging: stars (gas) Cold accretion gas Hot accretion gas A. Evans, HST Dekel et al 2007 Ocvirk et al 2008

8. Merging vs. star formation Channels for galaxies to build up stellar mass: –accretion by merging –direct star formation Which one channel prevails depends on stellar mass Mergers are essential to build up the stellar mass in massive galaxies Guo & White 2008 star formation minor mergers major mergers more massive

9. Identifying mergers Methods to identify mergers in observations: (see Mark’s talk last week) CAS (concentration-asymmetry-clumpiness, Conselice 2003) Gini-M20 (Lotz, Primack & Madau 2004) close galaxy pairs (Patton et al 2000) Each method may identify different populations of merging galaxies: dry mergers gas-rich mergers mergers with different mass ratios…  The interpretation of results is not straightforward

10. Identifying mergers CAS & Gini-M20: merger has already occurred structural methods CAS: A>0.35 & A>S Pairs: galaxies haven’t yet merged magnitudes within 1.5 physical separation < 30 kpc/h merging timescale ≤ 400 Myr mass ratio ≥ 0.25

11. The observed sample Low redshift data: mergers identified by structural asymmetries (CAS) –De Propris et al 2007 @ z=0 –Conselice et al 2009 (COSMOS + EGS) High redshift data: –structural asymmetry: Conselice et al 2008 (HDF+UDF) –galaxy pairs: Bluck et al 2009 (GOODS)

12. Mergers in the Millennium Most previous works use galaxy pairs: –Kitzbichler & White 2008 –Patton & Atfield 2008 –Mateus 2008 –Genel et al 2008, 2009 Bertone & Conselice 2009: direct counting of mergers in the simulation: –better consistency with CAS and Gini-M20 counts –no ambiguity from merging time-scales, mass ratios and pair separation

13. Counting mergers Procedure: stellar masses, mass ratios, time of merging are known from model set a time-scale: τ=0.4 Gyr and τ=1 Gyr: –τ is equivalent to the time-scale to which structural methods are sensitive to identify mergers in observations –investigate dependence on τ: source of uncertainty in obs at given redshift, count how many galaxies have undergone a merger within τ calculate merger rates, fractions etc

14. Merger fractions Definition: Fraction of galaxies that have undergone a merger within the last τ Gyr strongly dependent on τ more mergers at high redshift and in massive galaxies SB & Conselice 2009

15. Merger fraction vs. redshift good agreement at high stellar masses and z<2 observations underestimated by a large factor when low mass galaxies are considered

16. Gamma vs. redshift Γ= f gm / τ with f gm = 2f m /(1+f m ) average time between mergers  inverse of the merger rate per galaxy too long!

17. Merger rates Definition: in the simulation R is independent of the time-scale used to count mergers rate is highest for low mass galaxies visible evolution with redshift

18. Shape of merger rate vs. M * the shape of the merger rate vs. stellar mass is defined by the shape of the stellar mass function n gm (z) merger fraction vs stellar mass ≈2 orders of magnitude stellar mass function ≈5 orders of magnitude merger rate vs stellar mass ≈3 orders of magnitude

19. Merger rates vs. redshift Good agreement at high stellar masses shape vs. redshift well reproduced But: how can there be good agreement for galaxies with M>10 10 M  when the merger fractions disagree?

20. Something wrong at M>10 10 M  ? The merger rate agreement with obs for M * >10 10 M  is a coincidence  stellar mass density: overestimated by factor ≈10  merger fraction: underestimated by factor ≈10 too many galaxies and not enough mergers in the Millennium at M<10 11 M  ? stellar mass density vs. redshift

21. Merging times median merging time of satellites in simulation increases with redshift longest merging times for low mass galaxies at z ≤ 1 it is comparable or larger than the Hubble time! problem in the semi- analytic model? Median galaxy merging time

22. Comparison with other models De Lucia & Blaizot 2007: same merger trees, galaxy formation prescriptions and parameters different SN feedback model predicted rates and fractions differ by factor of a few, similar redshift evolution similar agreement with observations, sometimes worse

23. Other models Bower et al 2006: same DM evolution different SA model different merger trees better reproduces the high redshift data difference in results at high z: is it due to the SA modelling or to the merger trees? Mateus 2008

24. Pair fractions data Kitzbichler & White 2008: calibrate the relationship between the fraction of galaxy pairs and the merger rate at high z close galaxy pairs are a reliable tool for extracting the merger history of galaxies the merging times used to convert to merger rates are overestimated by at least a factor of 2 in current observations does not solve the discrepancy we find at high z with pair fraction data problem: the position of type 2 satellites in the Millennium is uncertain

25. What do we learn? The simulated merger history is very sensitive to the semi-analytic prescriptions: –differences in results between Bertone et al 2007, De Lucia & Blaizot 2007 and Bower et al 2006, even using same merger trees  dependence on global star formation history –merger time-scale too long for low mass galaxies? Can this help solve other problems of the models? Too many red satellite galaxies, too many low mass star galaxies…? Some quantities in observations not fully understood might also introduce uncertainties in the results: –time-scale for merging sensitivity (CAS and pairs) –mass ratios

26. Conclusions We have recovered the merger history of galaxies in the Millennium simulation: merger rates and fractions vs. redshift and stellar mass massive galaxies experience on average more merger events than less massive ones, but have a lower merger rate model results agree with observations for massive galaxies, but disagree when galaxies with 10 10 M  < M * < 10 11 M  are considered too few mergers in the simulation between low mass galaxies

28. Galaxy Formation Physics Cold Gas (ISM) Hot Gas (ICM) Winds (SN feedback) Stars recycling star formation cooling ejection re-incorporation shock-heating Black Hole accretion Bertone et al 2007