Related papers: Exploring galactic properties with machine learnin…
Working with 108,786 Sloan Digital Sky Survey low redshift galaxies we have examined the relation between galaxy mass, metallicity, radius, and star formation rates primarily in the central portions of galaxies. We subdivided the redshift…
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…
Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments.…
We combine star-formation histories derived from observations of high redshift galaxies with measurements of the z~0 relation between gas-phase metallicity, stellar mass, and star formation rate to make an explicit and completely empirical…
We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
We present a novel approach for classifying star-forming galaxies using photometric images. By utilizing approximately $124,000$ optical color composite images and spectroscopic data of nearby galaxies at $0.01<z<0.06$ from the Sloan…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
We present rest-frame optical spectra for a sample of 9 low-mass star-forming galaxies in the redshift range 1.5 < z < 3 which are gravitationally lensed by foreground clusters. We used Triplespec, an echelle spectrograph at the Palomar…
We present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos from N-body simulations. Our model is built using the extremely randomized tree (ERT) algorithm and takes subhalo properties over…
We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam…
Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
Accurate determinations of metallicity for large, complete stellar samples are essential for advancing various studies of the Milky Way. In this paper, we present a data-driven algorithm that leverages photometric data from the KiDS and the…
Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of…
We develop a simple analytical model that tracks galactic metallicities governed by star formation and feedback to gain insight from the observed galaxy stellar mass-metallicity relations over a large range of stellar masses and redshifts.…
The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain…
We present an integrated study of star formation and galactic stellar mass assembly from z=0.05-1.5 and galactic metallicity evolution from z=0.05-0.9 using a very large and highly spectroscopically complete sample selected by rest-frame…
Photometric redshift estimation is a key requirement for modern large-area surveys, where spectroscopic measurements are observationally prohibitive. Seyfert II galaxies provide a particularly challenging test case due to the combined…
Accurate galaxy stellar masses are crucial to better understand the physical mechanisms driving the galaxy formation process. We use synthetic star formation and metal enrichment histories predicted by the {\sc galform} galaxy formation…