Related papers: Photometric Analysis for Predicting Star Formation…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
We use sparse regression methods (SRM) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter halos. SRM are machine learning algorithms…
Fitting synthetic spectral energy distributions (SED) to the multi-band photometry of galaxies to derive their star formation rates (SFR), stellar masses, ages, etc. requires making a priori assumptions about their star formation histories…
The assessment of the relationship between radio continuum luminosity and star formation rate (SFR) is of crucial importance to make reliable predictions for the forthcoming ultra-deep radio surveys and to allow a full exploitation of their…
We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural…
We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…
Using artificial neural network (ANN) predictions of total infra-red luminosities (LIR), we compare the host galaxy star formation rates (SFRs) of ~21,000 optically selected active galactic nuclei (AGN), 466 low excitation radio galaxies…
Accurate estimation of photometric redshifts (photo-$z$s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own…
We present a new method to estimate the average star formation rate per unit stellar mass (SSFR) of a stacked population of galaxies. We combine the spectra of 600-1000 galaxies with similar stellar masses and parameterise the star…
Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to…
We present a study to determine how star formation contributes to galaxy growth since z=1.5 over five decades in galaxy stellar mass. We investigate the specific star formation rate (SSFR; star formation rate [SFR] per unit galaxy stellar…
Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation…
Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images,…
Accurate estimates of fundamental physical properties of galaxies, such as star formation rates (SFRs) or stellar masses, are essential for testing and constraining models of galaxy formation and evolution. Spectral energy distribution…
Recent high-redshift ($z>4$) spatially resolved observations with the James Webb Space Telesescope have shown the evolution of the star formation rate (SFR) surface density ($\Sigma_{\rm SFR}$) and its main sequence in the $\Sigma_{\rm…
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
Distant star-forming galaxies show a correlation between their star formation rates (SFR) and stellar masses, and this has deep implications for galaxy formation. Here, we present a study on the evolution of the slope and scatter of the…
Star-formation rates (SFRs) of galaxies are commonly calculated by converting the measured Halpha luminosities (L_Halpha) into current SFRs. This conversion is based on a constant initial mass function (IMF) independent of the total SFR. As…
We present the star-formation-rate -- stellar-mass (SFR-M$_\ast$) relation for galaxies in the CEERS survey at $4.5\leq z\leq 12$. We model the \jwst\ and \hst\ rest-UV and rest-optical photometry of galaxies with flexible star-formation…
One of the aims of LSST is to perform a systematic survey of star clusters and star forming regions (SFRs) in our Galaxy. In particular, the observations obtained with LSST will make a big difference in Galactic regions that have been…