Overview of sub-grid models in cosmological simulations · intercloud density too high cooling rate...
Transcript of Overview of sub-grid models in cosmological simulations · intercloud density too high cooling rate...
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Overview of sub-grid models in cosmological simulations
Joop Schaye (Leiden) (Yope Shea)
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Length Scales (cm)
universe observable10
galaxies of clusters10
galaxies10
clustersstar 10
distanceinterstar 10
radiistellar 10
IGMin distance cleinterparti10
ISMin distance cleinterparti10
starsin distance cleinterparti10
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18
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Cosmological
simulations
Subgrid
models
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Length Scales (cm)
universe observable10
galaxies of clusters10
galaxies10
clustersstar 10
distanceinterstar 10
radiistellar 10
IGMin distance cleinterparti10
ISMin distance cleinterparti10
starsin distance cleinterparti10
28
24
22
20
18
11
2
0
8
Cosmological
simulations
Subgrid
models
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Where to put the gap? • Transition from warm (T ~ 104 K) to cold, molecular
(T << 104 K) ISM expected at ΣH ~ 10 M
pc-2 (nH ~ 10-2 – 10-1 cm-3 in warm phase).
– Determined by dust column needed to shield UV
– Associated with sharp reduction in Jeans scale star formation
– Threshold decreases with metallicity and increases with UV
(JS 04, Krumholz+ 11, Glover & Clark 12, Clark & Glover 13, …)
• Well-posed challenge:
Resolve the Jeans scales down to the warm ISM
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• Resolving the warm phase requires:
- Particle mass << 107 M
- Spatial resolution << 1 kpc
• Resolving gas with nH ~ 101 cm-3 and T ~ 102 K requires :
- particle mass << 103 M
- spatial resolution << 10 pc
- Radiative transfer
- Complex chemistry
• Convergence requires resolving the Jeans scales:
Basic resolution requirements
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The cold phase is
still too demanding for cosmological simulations
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Subgrid models for cosmological hydro simulations
• Radiative cooling/heating
• Star formation
• Chemodynamics/stellar evolution
• Black holes and AGN feedback
• Galactic winds driven by feedback from SF
• Less conventional things. E.g.: – Turbulence (incl. mixing)
– Cosmic rays
– Dust
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Radiative cooling • Standard assumptions:
– H & He in photo-ionisation equilibrium (optically thin, UV background only)
– Metals in collisional ionisation equilibrium (though many studies still assume primordial abundances!)
• Recent developments (e.g. Wiersma, JS & Smith 09; Shen+
10; Vogelsberger+ 13, Aumer+ ‘13): – Metals also in photo-ionisation equilibrium
– Relative abundance variations
• Cutting edge/future: – Non-equilibrium ionization
– Radiative transfer
– Local radiation sources
– Molecules
– Dust
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Cooling: effect of non-equil. and photo-ionisation
Oppenheimer & JS (2013a) nH=10-4 cm-3, z=1, Z=Z
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AGN proximity zone fossils
nH=10-4 cm-3, T = 104 K, z=1, Z=Z
Oppenheimer & JS (2013b)
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AGN proximity zone fossils
nH=10-4 cm-3, T = 104 K, z=1, Z=Z
Oppenheimer & JS (2013b)
Most intergalactic metals may reside
in out-of-equilibrium AGN fossil zones!
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Star formation • Standard approach:
– Schmidt volume density law with threshold
– Parameters tuned to match observed Kennicutt-Schmidt surface density law
– Stochastic implementation
– Power-law EoS for gas above SF threshold
• Recent developments: – Pressure laws allow direct implementation of observed
surface density laws w/o tuning (JS & Dalla Vecchia 08)
– Metallicity-dependent or only molecular gas (JS 04, Gnedin+ 09, Krumholz+ 09, JS+ 10, Feldmann+ 11, Kuhlen+ 12, Christensen+ 12)
– Zoomed simulations: • Higher thresholds
• Cold ISM physics
)( *
m
)( *
n
g
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Kennicutt SF law = pressure law
JS & Dalla Vecchia (2008)
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Kennicutt SF law = pressure law
JS & Dalla Vecchia (2008)
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Kennicutt SF law = pressure law
JS & Dalla Vecchia (2008)
Pressure law reproduces observed surface density law independent of the EoS (which sets scale height) This it not the case for a volume density law!
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Galactic winds driven by SF • Winds may be:
– Energy-driven
– Momentum-driven
– Both
• Sources of energy/momentum: – Supernovae
– Radiation pressure: • On dust
• From photo-ionisation
• From trapping of Lyα
– Stellar winds
– Cosmic rays
– Combination of the above
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Galactic winds driven by SF: WARNINGS • Efficient feedback is required to match observations
• Feedback is often inefficient due to the numerical implementation…
• … but inefficient feedback is sometimes interpreted as a need for different physical processes
• Nearly all implementations are extremely crude (e.g. radiation pressure w/o radiative transfer)
• At the current resolution, the different feedback processes are hardly distinguishable
• Many hydro simulations use tricks that make them more like SAMs than you may think. E.g.: – Wind velocity depends on halo mass or dark matter velocity
dispersion (e.g. Okamoto+, Davé/Oppenheimer+, Viel+, Vogelsberger+)
– Temporarily turn off hydro for winds (e.g. Springel/Hernquist, Davé/Oppenheimer, Viel, Vogelsberger)
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Implementing FB: recognized problems
• Much of the mass in the ISM is in the cold phase (T << 104 K )
• Simulations do not model cold phase intercloud density too high
cooling rate too high
feedback too inefficient
SF insufficiently clustered feedback too inefficient
Simplest recipe: star particles inject thermal energy into surroundings (e.g. Katz et al. 1996)
Recognized problems:
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Driving winds: brute force solution
• Allow for a cold phase
• Increase SF threshold (only sensible for cold phase)
• Still require subgrid recipe, but on smaller scale
Problem: need very high resolution
Can only model a small number of galaxies (zoomed simulations)
Need to pick initial conditions (e.g. merger history)
(e.g. Ceverino & Klypin ‘07, Hopkins+ ’12, Ceverino+ 13)
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Driving winds: subgrid recipes • Multiphase particles (e.g. Marri & White ‘93, Scannapieco, Murante,
Aumer/White)
• Suppress cooling by hand (e.g. Gerritsen ‘97, Thacker, Stinson/Brook/Gibson/Governato/Maccio/Mayer/Wadsley/…)
• Inject momentum (i.e. kinetic feedback) (e.g. Navarro & White ’93, Springel/Hernquist, Davé/Oppenheimer, Teyssier, OWLS/GIMIC, Vogelsberger, …)
– Most relevant advantage: can decrease initial mass loading
• Temporarily decouple winds from the hydrodynamics (e.g. Springel/Hernquist ‘03, Davé/Oppenheimer, Viel, Vogelsberger, …)
• Multiple feedback processes (e.g. Stinson+ ’13, …)
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• Reality: SNe (or BHs) inject lots of energy in very little mass High temperatures
Long cooling times
Efficient feedback
• Simulations: inject energy in large gas mass Low heating temperatures
Short cooling times
Inefficient feedback
Implementing FB: less recognized problems
e.g. Kay+ ’03; Booth & JS ’09; Creasey+ ‘11; Dalla Vecchia & JS ‘12
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• The SNII of an SSP of mass m* can heat a mass mg,heat by
ΔT = 4x107 K (m*/ mg,heat)
• Reality: m* >> mg,heat initially ΔT >> 108 K
tc >> 108 yr (nH/1 cm-3)
• In simulations: m* ~ 0.01 - 0.1 mg,heat ΔT ~ 106 K
tc ~ 105 yr (nH/1 cm-3)
overcooling
• Note that in simulations (m*/ mg,heat) is independent of resolution!
Implementing FB: less recognized problems
Dalla Vecchia & JS (2012)
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Implementing thermal FB: requirements
• FB only efficient if heated resolution elements expand faster than they cool radiatively:
tc >> ts = h/cs
where h is the spatial resolution
• Adiabatic expansion does not change tc / ts (assuming Brehmsstrahlung)
• Required T depends on density and resolution
Dalla Vecchia & JS (2012)
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Implementing efficient thermal FB:
• ΔT determined by resolution
• Stochastic FB (see also Kay+ ’03)
given ΔT, fraction of available energy that is injected, fth, determines heating probability
• fth not predicted, unresolved thermal losses need to be calibrated
Dalla Vecchia & JS (2012)
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Mass outflow rate: 1010 M halo
• Particle mass 7x102 M
K10cm 1105
5.7
3/2
3-
H2
s
c Tn
t
t
• Max nH ~ 102 cm-3 insensitive to ΔT for ΔT ≥ 106.5 K
Dalla Vecchia & JS (2012)
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1010 M halo, edge-on, gas density
Dalla Vecchia & JS (2012)
ΔT = 106.5 K ΔT = 107.5 K
17.5 kpc/h
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Mass outflow rate: 1012 M halo
• Particle mass 7x104 M
K10cm 1101
5.7
3/2
3-
H2
s
c Tn
t
t
• Max nH ~ 103 cm-3 insensitive to ΔT for ΔT ≥ 107.5 K
Dalla Vecchia & JS (2012)
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1012 M halo, edge-on, gas density
Dalla Vecchia & JS (2012) 45 kpc/h
ΔT = 106.5 K ΔT = 107.5 K
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EAGLE: Evolution and Assembly of GaLaxies and their Environments
• Planck cosmology; 50, 100 Mpc boxes
• Jeans scale marginally resolved in warm ISM
• Gadget with – Anarchy (pressure-entropy SPH, time step limiter, …, Dalla
Vecchia in prep)
– Improved OWLS subgrid physics
• Thermal feedback from SF, cooling not turned off
• Feedback efficiency function of metallicity (accounts for unresolved thermal losses) – Naturally more efficient at lower mass and higher redshift
– Calibrated to observed z=0 galaxy mass function
• Virgo collaboration project
• Most active members: Booth, Bower, Crain, Dalla Vecchia, Frenk, Furlong, Jenkins, McCarthy, Rosas-Guevara, Schaller, Schaye (PI), Theuns
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Hydro solvers vs subgrid variations • Recently lots of good work on differences between
AMR, moving mesh, standard SPH, fancy SPH
• Effect of hydro solver difficult to isolate because – Different effective resolution
– Subgrid physics cannot be implemented identically
– Turning off feedback not a good solution as results unrealistic and hypersensitive to resolution
• At the current resolution, choice of hydro solver is generally much less important than choice of subgrid physics (e.g. Scannapieco+ 12)
• This should not be an excuse to do the hydro poorly
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Vogelsberger+ (2013)
Different hydro solvers vs different subgrid physics
Vogelsberger+ (2012)
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Cosmological hydro: Final thoughts • Predictions for stellar properties and for ISM/CGM are
currently limited by subgrid physics, particularly galactic winds Need to be careful about what questions to ask
SPH vs AMR secondary issue
Much to be gained from observations of gas around galaxies
• Predictions for intergalactic gas are more robust and limited by “real” physics, e.g. radiative transfer, non-equilibrium ionisation
• Large-scale simulations have become much better are reproducing observations thanks to: – Better (more efficient) recipes for feedback from star formation
– AGN feedback
– Increased resolution
• The gap is starting to be closed from above using zoomed simulations (many talks this week)