We test if the cosmological zoom-in simulations of isolated galaxies from the FIRE project reproduce the properties of ultra diffuse galaxies (UDGs). We show that outflows that dynamically heat galactic stars, together with a passively aging stellar population after imposed quenching, naturally reproduce the observed population of red UDGs, without the need for high spin haloes, or dynamical influence from their host cluster. We reproduce the range of surface brightness, radius, and absolute magnitude of the observed red UDGs by quenching simulated galaxies at a range of different times. They represent a mostly uniform population of dark matter-dominated dwarf galaxies with M _* ~ 10⁸ M_☉, low metallicity, and a broad range of ages; the more massive the UDGs, the older they are. The most massive red UDG in our sample(M _* ~ 3 × 10⁸ M_☉) requires quenching at z ~ 3 when its halo reached M _h ~ 10¹¹M_☉. Our simulated UDGs form with normal stellar-to-halo ratios and match the central enclosed masses and the velocity dispersions of the observed UDGs. Enclosed masses remain largely fixed across a broad range of quenching times because the central regions of their dark matter haloes complete their growth early. If our simulated dwarfs are not quenched, they evolve into bluer low surface brightness galaxies with M/L similar to observed field dwarfs. While our simulation sample covers a limited range of formation histories and halo masses, we predict that UDG is a common, and perhaps even dominant, galaxy type around M _* ~ 10⁸ M_☉, both in the field and in clusters.

Binary black holes are the primary endpoint of massive stars. Their properties provide a unique opportunity to constrain binary evolution, which remains poorly understood. We predict the main properties of binary black holes and their merger products in/around the Milky Way. We present the first combination of a high-resolution cosmological simulation of a Milky Way-mass galaxy with a binary population synthesis model in this context. The hydrodynamic simulation, taken from the FIRE project, provides a cosmologically realistic star formation history for the galaxy, its stellar halo, and satellites. During post-processing, we apply a metallicity-dependent evolutionary model to the star particles to produce individual binary black holes. We find that 7 × 105 binary black holes have merged in the model Milky Way, and 1.2 × 106 binaries are still present, with a mean mass of 28M⊙. Because the black hole progenitors are strongly biased towards low-metallicity stars, half reside in the stellar halo and satellites and a third were formed outside the main galaxy. The numbers and mass distribution of the merged systems is broadly compatible with the LIGO/Virgo detections. Our simplified binary evolution models predict that LISA will detect more than 20 binary black holes, but that electromagnetic observations will be challenging. Our method will allow for constraints on the evolution of massive binaries based on comparisons between observations of compact objects and the predictions of varying binary evolution models. We provide online data of our star formation model and binary black hole distribution.

Aims. The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2. Methods. A method known as "transfer learning" is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia/RAVE data set, which improves sensitivity to properties of the real Milky Way. Results. The result is a catalog that identifies ∼767 000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.