Related papers: Exploring galactic properties with machine learnin…
Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most…
The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured…
We present new accurate measurements of the physical properties of a statistically significant sample of 103 galaxies at z~2 using near-infrared spectroscopy taken as part of the 3D-HST survey. We derive redshifts, metallicities and star…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
We use a sample of 87 rest-frame UV-selected star-forming galaxies with mean spectroscopic redshift z=2.26 to study the correlation between metallicity and stellar mass at high redshift. Using stellar masses determined from SED fitting to…
In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to…
Using a sample of 299 Ha-selected galaxies at z~0.8, we study the relationship between galaxy stellar mass, gas-phase metallicity, and star formation rate (SFR), and compare to previous results. We use deep optical spectra obtained with the…
Context: The mass-metallicity relationship (MMR) of star-forming galaxies is well-established, however there is still some disagreement with respect to its exact shape and its possible dependence on other observables. Aims: We measure the…
We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculations derived from hydrodynamic simulations for events such as…
Euclid will image ~14000 deg^2 of the extragalactic sky at visible and NIR wavelengths, providing a dataset of unprecedented size and richness that will facilitate a multitude of studies into the evolution of galaxies. In the vast majority…
Evolution of galaxies through cosmic time has been widely studied at high redshift, but there are a few studies in this field at lower redshifts. However, low-redshifts studies will provide important clues to the evolution of galaxies,…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a…
The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-art machine learning model for constraining the mass…
We present a comprehensive observational study of the gas phase metallicity of star-forming galaxies from z ~ 0 -> 3. We combine our new sample of gravitationally lensed galaxies with existing lensed and non-lensed samples to conduct a…
We use the newest generation of the Starburst99/Mappings code to generate an extensive suite of models to facilitate detailed studies of star-forming galaxies and their ISM properties, particularly at low metallicities. The new models used…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models of the 2D metallicity distribution. However, so far these…
We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as…