- The Feedback in Realistic Environment (FIRE) Project
- MassiveFIRE (a FIRE project targeting massive galaxies)
- AGORA Galaxy Simulation Comparison Project (leading the AGORA effort of the Chicago-based ART team)
(incomplete list, random ordering)
Phil Hopkins, Rachel Bezanson, Jenny Greene, Mike Boylan-Kolchin, James Bullock, Daniel Anglés-Alcázar, Chris Hayward, Sarah Wellons, Claude-André Faucher-Giguère, Dušan Kereš, Mariska Kriek, Xiangcheng Ma, Lucio Mayer, Desika Narayanan, Eliot Quataert, Sedona Price, Justin Spilker, David Setton, Joachim Stadel
Hydrodynamic simulations provide a powerful, but computationally expensive, approach to study the interplay of dark matter and baryons in cosmological structure formation. Here we introduce the EMulating Baryonic EnRichment (EMBER) Deep Learning framework to predict baryon fields based on dark-matter-only simulations thereby reducing computational cost. EMBER comprises two network architectures, U-Net and Wasserstein Generative Adversarial Networks (WGANs), to predict two-dimensional gas and HI densities from dark matter fields. We design the conditional WGANs as stochastic emulators, such that multiple target fields can be sampled from the same dark matter input. For training we combine cosmological volume and zoom-in hydrodynamical simulations from the Feedback in Realistic Environments (FIRE) project to represent a large range of scales. Our fiducial WGAN model reproduces the gas and HI power spectra within 10% accuracy down to ~10 kpc scales. Furthermore, we investigate the capability of EMBER to predict high resolution baryon fields from low resolution dark matter inputs through upsampling techniques. As a practical application, we use this methodology to emulate high-resolution HI maps for a dark matter simulation of a L = 100 Mpc /h comoving cosmological box. The gas content of dark matter haloes and the HI column density distributions predicted by EMBER agree well with results of large volume cosmological simulations and abundance matching models. Our method provides a computationally efficient, stochastic emulator for augmenting dark matter only simulations with physically consistent maps of baryon fields.
LEO-Py: Estimating likelihoods for correlated, censored, and uncertain data with given marginal distributions
Data with uncertain, missing, censored, and correlated values are commonplace in many research fields including astronomy. Unfortunately, such data are often treated in an ad hoc way in the astronomical literature potentially resulting in inconsistent parameter estimates. Furthermore, in a realistic setting, the variables of interest or their errors may have non-normal distributions which complicates the modeling. I present a novel approach to compute the likelihood function for such data sets. This approach employs Gaussian copulas to decouple the correlation structure of variables and their marginal distributions resulting in a flexible method to compute likelihood functions of data in the presence of measurement uncertainty, censoring, and missing data. I demonstrate its use by determining the slope and intrinsic scatter of the star forming sequence of nearby galaxies from observational data. The outlined algorithm is implemented as the flexible, easy-to-use, open-source Python package LEO-Py.
We analyse the star formation rates (SFRs), colors, and dust extinctions of galaxies in massive (10^12.5-10^13.5 Msun) halos at z~2 in high-resolution, cosmological zoom-in simulations as part of the Feedback in Realistic Environments (FIRE) project. The simulations do not model feedback from active galactic nuclei (AGN) but reproduce well the observed relations between stellar and halo mass and between stellar mass and SFR. About half (a third) of the simulated massive galaxies (massive central galaxies) at z~2 have broad-band colors classifying them as 'quiescent', and the fraction of quiescent centrals is steeply decreasing towards higher redshift, in agreement with observations. The progenitors of z~2 quiescent central galaxies are, on average, more massive, have lower specific SFRs, and reside in more massive halos than the progenitors of similarly massive star forming centrals. The simulations further predict a morphological mix of galaxies that includes disk-dominated, irregular, and early-type galaxies. However, our simulations do not reproduce the reddest of the quiescent galaxies observed at z~2. We also do not find evidence for a color bimodality, but are limited by our modest sample size. In our simulations, the star formation activity of central galaxies of moderate mass (Mstar~10^10-10^11 Msun) is affected by a combination of two distinct physical processes. Outflows powered by stellar feedback result in a short-lived (<100 Myr), but almost complete, suppression of star formation activity after which many galaxies quickly recover and continue to form stars at normal rates. In addition, galaxies residing in slowly growing halos tend to experience a moderate reduction of their SFRs ('cosmological starvation'). The relative importance of these processes and AGN feedback is uncertain and will be explored in future work.
The cosmic noon (redshifts ~1.5–3) marked a period of vigorous star formation for most galaxies. However, about a third of the more massive galaxies at those times were quiescent in the sense that their observed stellar populations are inconsistent with rapid star formation. The reduced star formation activity is often attributed to gaseous outflows driven by feedback from supermassive black holes, but the impact of black hole feedback on galaxies in the young Universe is not yet definitively established. We analyse the origin of quiescent galaxies with the help of ultrahigh resolution, cosmological simulations that include feedback from stars but do not model the uncertain consequences of black hole feedback. We show that dark matter halos with specific accretion rates below ~0.25–0.4 Gyr^-1 preferentially host galaxies with reduced star formation rates and red broad-band colors. The fraction of such halos in large dark-matter-only simulations matches the observed fraction of moderately massive, quiescent galaxies (with stellar masses of ~10–100 billion solar masses). This suggest that halo accretion rate may be an important factor in deciding which massive galaxies at cosmic noon become quiescent. Empirical models that connect galaxy and halo evolution, such as halo occupation distribution or abundance matching models, assume a tight link between galaxy properties and the masses of their parent halos. These models will benefit from adding the specific accretion rate of halos as a second model parameter.
Submillimeter-luminous galaxies at high-redshift are the most luminous, heavily star-forming galaxies in the Universe, and are characterized by prodigious emission in the far-infrared at 850 microns (S850 > 5 mJy). They reside in halos of 10 trillion solar masses, have low gas fractions compared to main sequence disks at a comparable redshift, trace complex environments, and are not easily observable at optical wavelengths. Their physical origin remains unclear. Galaxy evolution simulations have been able to form galaxies with the requisite luminosities, but have otherwise been unable to simultaneously match the stellar masses, star formation rates, gas fractions and environments. We report on a cosmological hydrodynamic galaxy formation simulation that is able to form a submillimeter galaxy which simultaneously satisfies the broad range of observed physical constraints. We find that groups of galaxies residing in massive dark matter halos have rising star formation histories that peak at collective rates ~ 500-1000 solar masses per yr at redshift 2-3, by which time the interstellar medium is sufficiently enriched with metals that the region may be observed as a submillimeter-selected system. The intense star formation rates are fueled in part by a reservoir gas supply enabled by stellar feedback at earlier times, not through major mergers. With a duty cycle of nearly a gigayear, our simulations show that the submillimeter-luminous phase of high-z galaxies is a drawn out one that is associated with significant mass buildup in early Universe proto-clusters, and that many submillimeter-luminous galaxies are actually composed of numerous unresolved components (for which there is some observational evidence).
Cold Dark Matter (CDM) theory, a pillar of modern cosmology and astrophysics, predicts the existence of a large number of starless dark matter halos surrounding the Milky Way (MW). However, clear observational evidence of these "dark" substructures remains elusive. We propose a detection method of orbiting substructure that relies on the small velocity changes imposed on the stars in the MW disk. Using high-resolution numerical simulations we estimated that the new space telescope Gaia should detect the kinematic signatures of a few starless substructures provided the CDM paradigm holds. Such a measurement will provide unprecedented constraints on the primordial matter power spectrum at low-mass scales and offer a new handle onto the particle physics properties of dark matter.