Galaxy formation and evolution

Abstract:

We present the first localized detections of the cold neutral medium (CNM) in IC 10, offering a rare view of dense atomic gas in a low-metallicity (Z/Z⊙ ∼ 0.27) dwarf galaxy. As a low-metallicity starburst, IC 10’s interstellar medium conditions could reflect small scale physics conditions that mirror those of early galaxies, providing a unique window into the heating and cooling processes that shaped the interstellar medium in early-Universe environments. Leveraging the high angular (<5″ ∼ 15 pc) and spectral (0.4 km s−1) resolution of the Local Group L-band Survey, we searched for H I absorption against nine continuum radio sources and detected absorption along three sightlines corresponding to internal radio emission sources within IC 10. Using Gaussian decomposition and radiative transfer, we characterize the CNM, deriving spin temperatures of ∼30–55 K, column densities of (0.6–3.0) × 1021 cm−2, cold H I fractions of ∼21%–37%, and line widths of ∼5.6–13.6 km s−1. For each individual detection of H I absorption, we find corresponding molecular emission from 12CO (J = 1–0), HCO+ (J = 1–0), and HCN (J = 1–0) at similar velocities and with comparable line widths, indicating a well-mixed cold atomic and molecular medium. In IC 10, the CNM shows a clear kinematic connection to the high-density ISM, implying a stronger dynamical coupling with molecular gas than in the Milky Way, in line with expectations for low-metallicity environments. At the ∼15 pc scales probed by slightly extended H II regions in IC 10, unresolved CNM clouds likely contribute to line blending, so the observed broad H I line widths may partly reflect spatial and kinematic averaging.

Abstract:

Context . Estimating properties of star clusters from unresolved broadband photometry is a challenging problem that is classically tackled using spectral energy distribution (SED) fitting methods that are based on simple stellar population models. However, grid-based methods suffer from computational limitations. Because of their exponential scaling, they can become intractable when the number of inference parameters grows. In addition, nuisance parameters in the model can make the computation of the likelihood function intractable. These limitations can be overcome by modern generative deep learning methods that offer flexible and powerful tools for modeling high-dimensional posterior distributions and fast inference from learned data. Aims . We present a normalizing flow approach for the inference of cluster age, mass, and reddening parameters from Hubble Space Telescope broadband photometry. In particular, we explore our network’s behavior when dealing with an inference problem that has been analyzed in previous works. Methods . We used the SED modeling code CIGALE to create a dataset of synthetic photometric observations for 5 × 10 6 mock star clusters. Subsequently, this dataset was used to train a coupling-based flow in the form of a conditional invertible neural network to predict posterior probability distributions for cluster age, mass, and reddening from photometric observations. Results . We predicted cluster parameters for the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) Data Release 3 catalog. To evaluate the capabilities of the network, we compared our results to the publicly available PHANGS estimates and found that the estimates agree reasonably well. Conclusions . We demonstrate that normalizing flow methods can be a viable tool for the inference of cluster parameters, and argue that this approach is especially useful when nuisance parameters make the computation of the likelihood intractable and in scenarios that require efficient density estimation.

Abstract:

Light reprocessed by dust grains emitting in the infrared enables the study of the physics at play in dusty embedded regions, where ultraviolet and optical wavelengths are attenuated. Infrared telescopes such as JWST have made it possible to study the earliest feedback phases, when stars are shielded by cocoons of gas and dust. Comprehending this phase is crucial for unravelling the effects of feedback from young stars that leads to their emergence and the dispersal of their host molecular clouds. Here we show that the transition from the embedded to the exposed phase of star formation is short (< 4 Myr) and sometimes almost absent (< 1 Myr) across a sample of 37 nearby star-forming galaxies covering a wide range of morphologies, from massive barred spirals to irregular dwarfs. The short duration of the dust-clearing timescales suggests a predominant role of pre-supernova feedback mechanisms in revealing newborn stars, confirming previous results on smaller samples and allowing, for the first time, a statistical analysis of their dependencies. We find that the timescales associated with mid-infrared emission at 21 μm, tracing a dust-embedded feedback phase, are controlled by a complex interplay between giant molecular cloud properties (masses and velocity dispersions) and galaxy morphology. We report relatively longer durations of the embedded phase of star formation in barred spiral galaxies, while this phase is significantly reduced in low-mass irregular dwarf galaxies. We discuss tentative trends with gas-phase metallicity, which may favor faster cloud dispersal at low metallicities.