Galaxy merging in the Millennium simulation Serena Bertone - UC Santa Cruz Chris Conselice - U. Nottingham arXiv:0904.2365 MNRAS, in press Cosmoclub, April.
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Galaxy merging in the Millennium simulation
Serena Bertone - UC Santa Cruz
Chris Conselice - U. Nottingham
arXiv:0904.2365 MNRAS, in press
Cosmoclub, April 27th, 2009
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
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
4 public galaxy catalogues:• Croton et al 2005• Bower et al 2006• De Lucia & Blaizot 2007• Bertone, De Lucia & Thomas 2007
Cosmological modelIC from CMB:
WMAP(1)
DM evolutionMillenniumSpringel et al.
2005
N-body simulation
FOF group finder
Halo merger trees
Evolution of galaxies
SA galaxy formation model
Galaxy catalogues: observed galaxy
propertiesSB, De Lucia & Thomas 2007
stellar pop synt. modelsdust extinction etc
Galaxy Formation in a Nutshell
Galaxy Formation Physics
Cold Gas(ISM)
Hot Gas(ICM)
Winds(SN
feedback)
Stars
recycling
star formation
coolin
g
ejection
re-incorporation
shock-heating
Black Hole accretion
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
hotbubble
coldbubble
thin cold shell
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
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 2008star formation
minor mergersmajor mergers
more
massiv
e
Identifying mergersMethods 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
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
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)
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
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
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
€
fm =Nmergers (M*, z)
Ngalaxies (M*, z)
SB & Conselice 2009
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
Gamma vs. redshift• Γ= fgm / τ with fgm= 2fm/(1+fm)
• average time between mergers inverse of the merger rate per galaxy
too long!
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
€
R(z) =fgm (z) ⋅ngm (z)
τ
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
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?
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
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
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
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
Pair fractions dataKitzbichler & 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
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
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 1010 M < M* < 1011 M are considered
• too few mergers in the simulation between low mass galaxies
Galaxy Formation Physics
Cold Gas(ISM)
Hot Gas(ICM)
Winds(SN
feedback)
Stars
recycling
star formation
coolin
g
ejection
re-incorporation
shock-heating
Black Hole accretionBertone et al 2007
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