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ABSTRACT The dark matter (DM) distribution in dwarf galaxies provides crucial insights into both structure formation and the particle nature of DM. GraphNPE (Graph Neural Posterior Estimator), first introduced in Nguyen et al. (2023), is a novel simulation-based inference framework that combines graph neural networks and normalizing flows to infer the DM density profile from line-of-sight stellar velocities. Here, we apply GraphNPE to satellite dwarf galaxies in the FIRE-2 Latte simulation suite of Milky Way-mass haloes, testing it against both Cold and Self-Interacting DM scenarios. Our method demonstrates superior precision compared to conventional Jeans-based approaches, recovering DM density profiles to within the 95 per cent confidence level even in systems with as few as 30 tracers. Moreover, we present the first evaluation of mass modelling methods in constraining two key parameters from realistic simulations: the peak circular velocity, $$V_\mathrm{max}$$, and the peak virial mass, $$M_\mathrm{200m}^\mathrm{peak}$$. Using only line-of-sight velocities, GraphNPE can reliably recover both $$V_\mathrm{max}$$ and $$M_\mathrm{200m}^\mathrm{peak}$$ within our quoted uncertainties, including those experiencing tidal effects ($$\gtrsim 63~{{\rm per\ cent}}$$ of systems are recovered within our 68 per cent confidence intervals and $$\gtrsim 92~{{\rm per\ cent}}$$ within our 95 per cent confidence intervals). The method achieves $$10-20~{{\rm per\ cent}}$$ accuracy in $$V_\mathrm{max}$$ recovery, while $$M_\mathrm{200m}^\mathrm{peak}$$ is recovered to $$0.1-0.4 \, \mathrm{dex}$$ accuracy. This work establishes GraphNPE as a robust tool for inferring DM density profiles in dwarf galaxies, offering promising avenues for constraining DM models. The framework’s potential extends beyond this study, as it can be adapted to non-spherical and disequilibrium models, showcasing the broader utility of simulation-based inference and graph-based learning in astrophysics. 

Abstract Stellar streams from globular clusters (GCs) offer constraints on the nature of dark matter and have been used to explore the dark matter halo structure and substructure of our Galaxy. Detection of GC streams in other galaxies would broaden this endeavor to a cosmological context, yet no such streams have been detected to date. To enable such exploration, we develop the Hough Stream Spotter ( HSS ), and apply it to the Pan-Andromeda Archaeological Survey (PAndAS) photometric data of resolved stars in M31's stellar halo. We first demonstrate that our code can re-discover known dwarf streams in M31. We then use the HSS to blindly identify 27 linear GC stream-like structures in the PAndAS data. For each HSS GC stream candidate, we investigate the morphologies of the streams and the colors and magnitudes of all stars in the candidate streams. We find that the five most significant detections show a stronger signal along the red giant branch in color–magnitude diagrams than spurious non-stream detections. Lastly, we demonstrate that the HSS will easily detect globular cluster streams in future Nancy Grace Roman Space Telescope data of nearby galaxies. This has the potential to open up a new discovery space for GC stream studies, GC stream gap searches, and for GC stream-based constraints on the nature of dark matter. 

Abstract We present the empirical dust attenuation (EDA) framework—a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations for three state-of-the-art large-scale cosmological hydrodynamical simulations: SIMBA, IllustrisTNG, and EAGLE. We then compare the optical and UV color–magnitude relations, ( g − r ) − M r and (far-UV −near-UV) − M r , of the simulations to a M r < − 20 and UV complete Sloan Digital Sky Survey galaxy sample using likelihood-free inference. Without dust, none of the simulations match observations, as expected. With the EDA, however, we can reproduce the observed color–magnitude with all three simulations. Furthermore, the attenuation curves predicted by our dust prescription are in good agreement with the observed attenuation–slope relations and attenuation curves of star-forming galaxies. However, the EDA does not predict star-forming galaxies with low A V since simulated star-forming galaxies are intrinsically much brighter than observations. Additionally, the EDA provides, for the first time, predictions on the attenuation curves of quiescent galaxies, which are challenging to measure observationally. Simulated quiescent galaxies require shallower attenuation curves with lower amplitude than star-forming galaxies. The EDA, combined with forward modeling, provides an effective approach for shedding light on dust in galaxies and probing hydrodynamical simulations. This work also illustrates a major limitation in comparing galaxy formation models: by adjusting dust attenuation, simulations that predict significantly different galaxy populations can reproduce the same UV and optical observations. 

ABSTRACT Understanding the rate at which stars form is central to studies of galaxy formation. Observationally, the star formation rates (SFRs) of galaxies are measured using the luminosity in different frequency bands, often under the assumption of a time-steady SFR in the recent past. We use star formation histories (SFHs) extracted from cosmological simulations of star-forming galaxies from the FIRE project to analyse the time-scales to which the H α and far-ultraviolet (FUV) continuum SFR indicators are sensitive. In these simulations, the SFRs are highly time variable for all galaxies at high redshift, and continue to be bursty to z = 0 in dwarf galaxies. When FIRE SFHs are partitioned into their bursty and time-steady phases, the best-fitting FUV time-scale fluctuates from its ∼10 Myr value when the SFR is time-steady to ≳100 Myr immediately following particularly extreme bursts of star formation during the bursty phase. On the other hand, the best-fitting averaging time-scale for H α is generally insensitive to the SFR variability in the FIRE simulations and remains ∼5 Myr at all times. These time-scales are shorter than the 100 and 10 Myr time-scales sometimes assumed in the literature for FUV and H α, respectively, because while the FUV emission persists for stellar populations older than 100 Myr, the time-dependent luminosities are strongly dominated by younger stars. Our results confirm that the ratio of SFRs inferred using H α versus FUV can be used to probe the burstiness of star formation in galaxies.