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Register a new userModeling for Stellar Feedback in Galaxy Formation Simulations
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Added by
Alejandro N\\'u\\~nez
Publication date
2017
fields
Physics
and research's language is
English
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Various heuristic approaches to model unresolved supernova (SN) feedback in galaxy formation simulations exist to reproduce the formation of spiral galaxies and the overall inefficient conversion of gas into stars. Some models, however, require resolution dependent scalings. We present a sub-resolution model representing the three major phases of supernova blast wave evolution $-$free expansion, energy conserving Sedov-Taylor, and momentum conserving snowplow$-$ with energy scalings adopted from high-resolution interstellar-medium simulations in both uniform and multiphase media. We allow for the effects of significantly enhanced SN remnant propagation in a multiphase medium with the cooling radius scaling with the hot volume fraction, $f_{mathrm{hot}}$, as $(1 - f_{mathrm{hot}})^{-4/5}$. We also include winds from young massive stars and AGB stars, Stromgren sphere gas heating by massive stars, and a gas cooling limiting mechanism driven by radiative recombination of dense HII regions. We present initial tests for isolated Milky-Way like systems simulated with the GADGET based code SPHgal with improved SPH prescription. Compared to pure thermal SN input, the model significantly suppresses star formation at early epochs, with star formation extended both in time and space in better accord with observations. Compared to models with pure thermal SN feedback, the age at which half the stellar mass is assembled increases by a factor of 2.4, and the mass loading parameter and gas outflow rate from the galactic disk increase by a factor of 2. Simulation results are converged for a two order of magnitude variation in particle mass in the range (1.3$-$130)$times 10^4$ solar masses.
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Direct comparisons between galaxy simulations and observations that both reach scales < 100 pc are strong tools to investigate the cloud-scale physics of star formation and feedback in nearby galaxies. Here we carry out such a comparison for hydrodynamical simulations of a Milky Way-like galaxy, including stochastic star formation, HII region and supernova feedback, and chemical post-processing at 8 pc resolution. Our simulation shows excellent agreement with almost all kpc-scale and larger observables, including total star formation rates, radial profiles of CO, HI, and star formation through the galactic disc, mass ratios of the ISM components, both whole-galaxy and resolved Kennicutt-Schmidt relations, and giant molecular cloud properties. However, we find that our simulation does not reproduce the observed de-correlation between tracers of gas and star formation on < 100 pc scales, known as the star formation uncertainty principle, which indicates that observed clouds undergo rapid evolutionary lifecycles. We conclude that the discrepancy is driven by insufficiently-strong pre-supernova feedback in our simulation, which does not disperse the surrounding gas completely, leaving star formation tracer emission too strongly associated with molecular gas tracer emission, inconsistent with observations. This result implies that the cloud-scale de-correlation of gas and star formation is a fundamental test for feedback prescriptions in galaxy simulations, one that can fail even in simulations that reproduce all other macroscopic properties of star-forming galaxies.
Galaxy formation simulations frequently use Initial Mass Function (IMF) averaged feedback prescriptions, where star particles are assumed to represent single stellar populations that fully sample the IMF. This approximation breaks down at high mass resolution, where stochastic variations in stellar populations become important. We discuss various schemes to populate star particles with stellar masses explicitly sampled from the IMF. We use Monte Carlo numerical experiments to examine the ability of the schemes to reproduce an input IMF in an unbiased manner while conserving mass. We present our preferred scheme which can easily be added to pre-existing star formation prescriptions. We then carry out a series of high resolution isolated simulations of dwarf galaxies with supernovae, photoionization and photoelectric heating to compare the differences between using IMF averaged feedback and explicitly sampling the IMF. We find that if supernovae are the only form of feedback, triggering individual supernovae from IMF averaged rates gives identical results to IMF sampling. However, we find that photoionization is more effective at regulating star formation when IMF averaged rates are used, creating more, smaller H II regions than the rare, bright sources produced by IMF sampling. We note that the increased efficiency of the IMF averaged feedback versus IMF sampling is not necessarily a general trend and may be reversed depending on feedback channel, resolution and other details. However, IMF sampling is always the more physically motivated approach. We conservatively suggest that it should be used for star particles less massive than $sim500,mathrm{M_odot}$.
Radiative feedback (RFB) from stars plays a key role in galaxies, but remains poorly-understood. We explore this using high-resolution, multi-frequency radiation-hydrodynamics (RHD) simulations from the Feedback In Realistic Environments (FIRE) project. We study ultra-faint dwarf through Milky Way mass scales, including H+He photo-ionization; photo-electric, Lyman Werner, Compton, and dust heating; and single+multiple scattering radiation pressure (RP). We compare distinct numerical algorithms: ray-based LEBRON (exact when optically-thin) and moments-based M1 (exact when optically-thick). The most important RFB channels on galaxy scales are photo-ionization heating and single-scattering RP: in all galaxies, most ionizing/far-UV luminosity (~1/2 of lifetime-integrated bolometric) is absorbed. In dwarfs, the most important effect is photo-ionization heating from the UV background suppressing accretion. In MW-mass galaxies, meta-galactic backgrounds have negligible effects; but local photo-ionization and single-scattering RP contribute to regulating the galactic star formation efficiency and lowering central densities. Without some RFB (or other rapid FB), resolved GMCs convert too-efficiently into stars, making galaxies dominated by hyper-dense, bound star clusters. This makes star formation more violent and bursty when SNe explode in these hyper-clustered objects: thus, including RFB smoothes SFHs. These conclusions are robust to RHD methods, but M1 produces somewhat stronger effects. Like in previous FIRE simulations, IR multiple-scattering is rare (negligible in dwarfs, ~10% of RP in massive galaxies): absorption occurs primarily in normal GMCs with A_v~1.
Cosmological simulations of galaxies have typically produced too many stars at early times. We study the global and morphological effects of radiation pressure (RP) in eight pairs of high-resolution cosmological galaxy formation simulations. We find that the additional feedback suppresses star formation globally by a factor of ~2. Despite this reduction, the simulations still overproduce stars by a factor of ~2 with respect to the predictions provided by abundance matching methods for halos more massive than 5E11 Msun/h (Behroozi, Wechsler & Conroy 2013). We also study the morphological impact of radiation pressure on our simulations. In simulations with RP the average number of low mass clumps falls dramatically. Only clumps with stellar masses Mclump/Mdisk <= 5% are impacted by the inclusion of RP, and RP and no-RP clump counts above this range are comparable. The inclusion of RP depresses the contrast ratios of clumps by factors of a few for clump masses less than 5% of the disk masses. For more massive clumps, the differences between and RP and no-RP simulations diminish. We note however, that the simulations analyzed have disk stellar masses below about 2E10 Msun/h. By creating mock Hubble Space Telescope observations we find that the number of clumps is slightly reduced in simulations with RP. However, since massive clumps survive the inclusion of RP and are found in our mock observations, we do not find a disagreement between simulations of our clumpy galaxies and observations of clumpy galaxies. We demonstrate that clumps found in any single gas, stellar, or mock observation image are not necessarily clumps found in another map, and that there are few clumps common to multiple maps.
Large-scale cosmological simulations of galaxy formation currently do not resolve the densities at which molecular hydrogen forms, implying that the atomic-to-molecular transition must be modeled either on the fly or in postprocessing. We present an improved postprocessing framework to estimate the abundance of atomic and molecular hydrogen and apply it to the IllustrisTNG simulations. We compare five different models for the atomic-to-molecular transition, including empirical, simulation-based, and theoretical prescriptions. Most of these models rely on the surface density of neutral hydrogen and the ultraviolet (UV) flux in the Lyman-Werner band as input parameters. Computing these quantities on the kiloparsec scales resolved by the simulations emerges as the main challenge. We show that the commonly used Jeans length approximation to the column density of a system can be biased and exhibits large cell-to-cell scatter. Instead, we propose to compute all surface quantities in face-on projections and perform the modeling in two dimensions. In general, the two methods agree on average, but their predictions diverge for individual galaxies and for models based on the observed midplane pressure of galaxies. We model the UV radiation from young stars by assuming a constant escape fraction and optically thin propagation throughout the galaxy. With these improvements, we find that the five models for the atomic-to-molecular transition roughly agree on average but that the details of the modeling matter for individual galaxies and the spatial distribution of molecular hydrogen. We emphasize that the estimated molecular fractions are approximate due to the significant systematic uncertainties.
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