Related papers: {\sc mirkwood:} Fast and Accurate SED Modeling Usi…
State-of-the-art spectral energy distribution (SED) analyses use a Bayesian framework to infer the physical properties of galaxies from observed photometry or spectra. They require sampling from a high-dimensional space of SED model…
We study spatially resolved properties (on spatial scales of $\sim 1-2$ kpc out to at least $3$ effective radii) of the stars, dust, and gas in ten nearby spiral galaxies. The properties of the stellar population and dust are derived by…
Galaxy spectral energy distributions (SEDs) remain among the most challenging yet informative quantities to reproduce in simulations due to the large and complex mixture of physical processes that shape the radiation output of a galaxy.…
Dust plays a pivotal role in determining the observed spectral energy distribution (SED) of galaxies. Yet our understanding of dust attenuation is limited and our observations suffer from the dust-metallicity-age degeneracy in SED fitting…
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not…
The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from multi-band imaging surveys. The accuracy of the photometric redshifts measured in these surveys…
In this paper, we present spectrophotometric models for galaxies of different morphological type whose spectral energy distributions (SEDs) take into account the effect of dust in absorbing UV-optical light and re-emitting it in the…
Robust estimation of star formation rates (SFRs) at higher redshifts (z>1) using UV-optical-NIR photometry is contingent on the ability of spectral energy distribution (SED) fitting to simultaneously constrain the dust attenuation, stellar…
Spectral energy distribution (SED) models of galaxies at $0.3\leq z \leq 2.5$ were constructed to be processed by the Euclid spectroscopic channel simulator in order to investigate the \Euclid Near-Infrared Spectrometer and Photometer…
We have fit the far-ultraviolet (FUV) to mid-infrared (MIR) spectral energy distributions (SEDs) for several nearby galaxies ($<$ 20 Mpc). Global, radial, and local photometric measurements are explored to better understand how SED-derived…
Many studies conclude that galaxies quench from the inside-out by examining profiles of specific star-formation rate (sSFR). These are usually measured by fitting spectral energy distributions (SEDs) assuming a fixed dust law and uniform…
End-to-end deep learning models fed with multi-band galaxy images are powerful data-driven tools used to estimate galaxy physical properties in the absence of spectroscopy. However, due to a lack of interpretability and the associational…
The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass ($M_*$), star formation rate (${\rm SFR}$), stellar metallicity ($Z_{\rm MW}$), and stellar age…
We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral…
We have a developed a new method for fitting spectral energy distributions (SEDs) to identify and constrain the physical properties of high-redshift (4 < z < 8) galaxies. Our approach uses an implementation of Bayesian based Markov Chain…
The properties of unresolved protostars and their local environment (e.g., disk, envelope and outflow characteristics) are frequently inferred from spectral energy distributions (SEDs) through comparison with idealized model SEDs. However,…
Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging CatBoost and deep learning architectures, we utilize…
Accurately measuring stellar parameters is a key goal to increase our understanding of the observable universe. However, current methods are limited by many factors, in particular, the biases and physical assumptions that are the basis for…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
The current and forthcoming observations of large samples of high-redshift galaxies selected according to various photometric and spectroscopic criteria can be interpreted in the context of galaxy formation, by means of models of evolving…