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ABSTRACT We analyse the stellar distributions on the [Fe/H]–[Mg/Fe] plane for 11 Milky Way-mass galaxies from the FIRE-2 cosmological baryonic zoom-in simulations. Alpha-element bimodality, in the form of two separate sequences on the [Fe/H]–[Mg/Fe] plane, is not a universal feature of disc galaxies. Five galaxies demonstrate double sequences with the $$\alpha$$-enriched one being older and kinematically hotter, in qualitative agreement with the high-$$\alpha$$ and low-$$\alpha$$ populations in the Milky Way disc; three galaxies have unimodal distribution, two show weakly bimodal features where low-$$\alpha$$ sequence is visible only over a short range of metallicities, and one show strong bimodality with a different slope of high-$$\alpha$$ population. We examine the galaxies’ gas accretion history over the last 8 Gyr, when bimodal sequences emerge, and demonstrate that the presence of the low-$$\alpha$$ sequence in the bimodal galaxies is related to the recent infall of metal-poor gas from the circumgalactic medium that joins the galaxy in the outskirts and induces significant growth of the gas discs compared to their non-bimodal counterparts. We also analyse the sources of the accreted gas and illustrate that both gas-rich mergers and smooth accretion of ambient gas can be the source of the accreted gas, and create slightly different bimodal patterns. 

Abstract Open-star clusters are the essential building blocks of the Galactic disk; “strong chemical tagging”—the premise that all star clusters can be reconstructed given chemistry information alone—is a driving force behind many current and upcoming large Galactic spectroscopic surveys. In this work, we characterize the abundance patterns for nine elements (C, N, O, Ne, Mg, Si, S, Ca, and Fe) in open clusters (OCs) in three galaxies (m12i, m12f, and m12m) from the Latte suite of FIRE-2 simulations, to investigate the feasibility of strong chemical tagging in these simulations. We select young massive (≥10^4.6M_⊙) OCs formed in the last ∼100 Myr and calculate the intra- and intercluster abundance scatter for these clusters. We compare these results with analogous calculations drawn from observations of OCs in the Milky Way. We find the intracluster scatter of the observations and simulations to be comparable. While the abundance scatter within each cluster is minimal (≲0.020 dex), the mean abundance patterns of different clusters are not unique. We also calculate the chemical difference in intra- and intercluster star pairs and find it, in general, to be so small that it is difficult to distinguish between stars drawn from the same OC or from different OCs. Despite tracing three distinct nucleosynthetic families (core-collapse supernovae, white dwarf supernovae, and stellar winds), we conclude that these elemental abundances do not provide enough discriminating information to use strong chemical tagging for reliable OC membership. 

Abstract The identity of dark matter has remained surprisingly elusive. While terrestrial experiments may be able to nail down a model, an alternative method is to identify dark matter based on astrophysical or cosmological signatures. A particularly sensitive approach is based on the unique signature of dark matter substructure in galaxy–galaxy strong lensing images. Machine-learning applications have been explored for extracting this signal. Because of the limited availability of high-quality strong lensing images, these approaches have exclusively relied on simulations. Due to the differences with the real instrumental data, machine-learning models trained on simulations are expected to lose accuracy when applied to real data. Here domain adaptation can serve as a crucial bridge between simulations and real data applications. In this work, we demonstrate the power of domain adaptation techniques applied to strong gravitational lensing data with dark matter substructure. We show with simulated data sets representative of Euclid and Hubble Space Telescope observations that domain adaptation can significantly mitigate the losses in the model performance when applied to new domains. Lastly, we find similar results utilizing domain adaptation for the problem of lens finding by adapting models trained on a simulated data set to one composed of real lensed and unlensed galaxies from the Hyper Suprime-Cam. This technique can help domain experts build and apply better machine-learning models for extracting useful information from the strong gravitational lensing data expected from the upcoming surveys. 

ABSTRACT Milky Way-mass galaxies in the FIRE-2 simulations demonstrate two main modes of star formation. At high redshifts star formation occurs in a series of short and intense bursts, while at low redshifts star formation proceeds at a steady rate with a transition from one mode to another at times ranging from 3 to 7 Gyr ago for different galaxies. We analyse how the mode of star formation affects iron and alpha-element abundance. We find that the early bursty regime imprints a measurable pattern in stellar elemental abundances in the form of a ‘sideways chevron’ shape on the [Fe/H] – [O/Fe] plane and the scatter in [O/Fe] at a given stellar age is higher than when a galaxy is in the steady regime. That suggests that the evolution of [O/Fe] scatter with age provides an estimate of the end of the bursty phase. We investigate the feasibility of observing of this effect by adding mock observational errors to a simulated stellar survey and find that the transition between the bursty and steady phase should be detectable in the Milky Way, although larger observational uncertainties make the transition shallower. We apply our method to observations of the Milky Way from the Second APOKASC Catalogue and estimate that the transition to steady star formation in the Milky Way happened 7 – 8 Gyrs ago, earlier than transition times measured in the simulations.