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Galaxy merging in the Millennium simulation

Cosmoclub , April 27 th , 2009. Galaxy merging in the Millennium simulation . Serena Bertone - UC Santa Cruz Chris Conselice - U. Nottingham. arXiv:0904.2365 MNRAS, in press. O verview. The Millennium simulation Techniques to identify mergers: in observations in simulations

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Galaxy merging in the Millennium simulation

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

  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 MDM>1010 M 4public galaxy catalogues: • Croton et al 2005 • Bower et al 2006 • De Lucia & Blaizot 2007 • Bertone, De Lucia & Thomas 2007

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

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

  6. PhysicsofWinds Bertoneet al 2007 SA model: • samephysicalmodelas De Lucia & Blaizot 2007 • New: SN windsmodelledasastrophysicalblastwavesin a cosmologicalcontext(Ostriker & McKee 1988) • two-phasemodelfor the long-termevolutionofwinds • adiabatic, pressure-driven expansion (Hoopeset al 2004, Strickland & Stevens 2000) • momentum-driven snowplough (Aguirreet al 2001, Theunset al 2001) hot bubble  cold bubble  thin cold shell

  7. How do galaxies accrete mass? A. Evans, HST • Merging: stars (gas) • Cold accretion gas • Hot accretion gas 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 more massive minor mergers major mergers Guo & White 2008 star formation

  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 τGyr • strongly dependent onτ • 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= fgm / τ with fgm= 2fm/(1+fm) • 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 ngm(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>1010 M when the merger fractions disagree?

  20. Something wrong at M>1010 M? • The merger rate agreement with obs for M*>1010 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<1011 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. redshiftand 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 1010 M < M* < 1011 M are considered • too few mergers in the simulation between low mass galaxies

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

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