Search for: All records

ABSTRACT The observationally inferred size versus stellar–mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies to explore how the predicted SMR changes depending on whether one uses star-particle counts directly or mock observations. We reproduce the SMR found in The Exploration of Local Volume Satellites survey remarkably well only when we infer stellar masses and sizes using mock observations. However, when we use star particles to directly infer stellar masses and half-mass radii, we find that our galaxies are too large and obey an SMR with too little scatter compared to observations. This discrepancy between the ‘true’ galaxy size and mass and those derived in the mock observation approach is twofold. First, our simulated galaxies have higher and more varied mass-to-light ratios (MLR) at a fixed colour than those commonly adopted, which tends to underestimate their stellar masses compared to their true, simulated values. Second, our galaxies have radially increasing MLR gradients therefore using a single MLR tends to underpredict the mass in the outer regions. Similarly, the true half-mass radius is larger than the half-light radius because the light is more concentrated than the mass. If our simulations are accurate representations of the real Universe, then the relationship between galaxy size and stellar mass is even tighter for low-mass galaxies than is commonly inferred from observed relations. 

ABSTRACT Forward-modeling observables from galaxy simulations enables direct comparisons between theory and observations. To generate synthetic spectral energy distributions (SEDs) that include dust absorption, re-emission, and scattering, Monte Carlo radiative transfer is often used in post-processing on a galaxy-by-galaxy basis. However, this is computationally expensive, especially if one wants to make predictions for suites of many cosmological simulations. To alleviate this computational burden, we have developed a radiative transfer emulator using an artificial neural network (ANN), ANNgelina, that can reliably predict SEDs of simulated galaxies using a small number of integrated properties of the simulated galaxies: star formation rate, stellar and dust masses, and mass-weighted metallicities of all star particles and of only star particles with age <10 Myr. Here, we present the methodology and quantify the accuracy of the predictions. We train the ANN on SEDs computed for galaxies from the IllustrisTNG project’s TNG50 cosmological magnetohydrodynamical simulation. ANNgelina is able to predict the SEDs of TNG50 galaxies in the ultraviolet (UV) to millimetre regime with a typical median absolute error of ∼7 per cent. The prediction error is the greatest in the UV, possibly due to the viewing-angle dependence being greatest in this wavelength regime. Our results demonstrate that our ANN-based emulator is a promising computationally inexpensive alternative for forward-modeling galaxy SEDs from cosmological simulations. 

ABSTRACT Negative feedback from accreting supermassive black holes is considered crucial in suppressing star formation and quenching massive galaxies. However, several models and observations suggest that black hole feedback may have a positive effect, triggering star formation by compressing interstellar medium gas to higher densities. We investigate the dual role of black hole feedback using cosmological hydrodynamic simulations from the Feedback In Realistic Environment (FIRE) project, incorporating a novel implementation of hyper-refined accretion-disc winds. Focusing on a massive, star-forming galaxy at z ∼ 2 ($$M_{\rm halo} \sim 10^{12.5}\, {\rm M}_{\odot }$$), we demonstrate that strong quasar winds with a kinetic power of ∼1046 erg s−1, persisting for over 20 Myr, drive the formation of a central gas cavity and significantly reduce the surface density of star formation across the galaxy’s disc. The suppression of star formation primarily occurs by limiting the availability of gas for star formation rather than by evacuating the pre-existing star-forming gas reservoir (preventive feedback dominates over ejective feedback). Despite the overall negative impact of quasar winds, we identify several potential indicators of local positive feedback, including (1) the spatial anticorrelation between wind-dominated regions and star-forming clumps, (2) higher local star formation efficiency in compressed gas at the edge of the cavity, and (3) increased contribution of outflowing material to local star formation. Moreover, stars formed under the influence of quasar winds tend to be located at larger radial distances. Our findings suggest that both positive and negative AGN feedback can coexist within galaxies, although the local positive triggering of star formation has a minor influence on global galaxy growth.