Related papers: Exploring galactic properties with machine learnin…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
Metallicity appears to be one the most important tool to study formation and evolution of galaxies. Recently, we have shown that metallicity of local galaxies is tightly related not only to stellar mass, but also to star formation rate…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
There is a well known correlation between the mass and metallicity of star-forming galaxies. Because mass is correlated with luminosity, this relation is often exploited, when spectroscopy is not available, to estimate galaxy metallicities…
We develop a machine learning-based framework to predict the HI content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental parameters. We train the algorithm on z=0-2 outputs from the…
The stellar metallicity is a direct measure of the amount of metals present in a galaxy, as a large part of the metals lie in its stars. In this paper we investigate new stellar metallicity indicators suitable for high-z galaxies studying…
Modern sky surveys produce vast amounts of observational data, making the application of classical methods for estimating galaxy properties challenging and time-consuming. This challenge can be significantly alleviated by employing…
Context. Understanding the Milky Way's formation and evolution across cosmic epochs necessitates precise stellar age determination across all Galactic components. Recent advancements in asteroseismology, spectroscopy, stellar modelling, and…
The chemical composition of high redshift galaxies is an important property which gives clues to their past history and future evolution and yet is difficult to measure with current techniques. In this paper we investigate new metallicity…
We study the impact of different galaxy statistics and empirical metallicity scaling relations on the merging rates and on the properties of compact objects binaries. First, we analyze the similarities and differences of using the star…
The chemical composition of the gas in galaxies versus cosmic time provides a very important tool for understanding galaxy evolution. Although there are many studies at high redshift, they are rather scarce at lower redshifts. However, low…
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…
Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared photometry from the Two Micron All-Sky Survey (2MASS).…
Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic…
Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS}…
Measuring distances of cosmological sources such as galaxies, stars and quasars plays an increasingly critical role in modern cosmology. Obtaining the optical spectrum and consequently calculating the redshift as a distance indicator could…
We present a robust method to estimate the redshift of galaxies using Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of 2313724…
With current and upcoming experiments such as WFIRST, Euclid and LSST, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data…
We report the stellar mass functions obtained from 20 radiation hydrodynamical simulations of star cluster formation in 500 M$_\odot$ molecular clouds with metallicities of 3, 1, 1/10 and 1/100 of the solar value, with the clouds subjected…
Aims. We present a continuation of our study about the relation between stellar mass and gas-phase metallicity in the VIMOS VLT Deep Survey (VVDS). In this work we extend the determination of metallicities up to redshift = 1.24 for a sample…