相关论文: Using machine learning to investigate the populati…
(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…
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.…
The ASSESS project aims to determine the role of episodic mass-loss in the evolution of massive stars. As a first step, we construct a catalog of spectroscopically identified dusty, evolved massive stars in ten southern galaxies for which…
Episodic mass loss is not understood theoretically, neither accounted for in state-of-the-art models of stellar evolution, which has far-reaching consequences for many areas of astronomy. We introduce the ERC-funded ASSESS project…
Mass loss in massive stars is crucial to understanding how these stars evolve and explode. Despite increasing evidence indicating its importance, episodic mass loss remains poorly understood. Here we report the results of an optical…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
The role of episodic mass loss in massive star evolution is one of the most important open questions of current stellar evolution theory. Episodic mass loss produces dust and therefore causes evolved massive stars to be very luminous in the…
Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying…
The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar…
Mass loss is one of the key parameters that determine stellar evolution. Despite the progress we have achieved over the last decades we still cannot match the observational derived values with theoretical predictions. Even worse, there are…
We focus on an analysis of galaxies of high stellar mass and low metallicity. We cross-correlated the Millenium Galaxy Catalogue (MGC) and the Sloan Digital Sky Survey (SDSS) galaxy catalogue to provide a sample of MGC objects with high…
Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…
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…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars in galaxies. Starting from a grid of theoretical population synthesis models we constructed a set of model…
Red supergiants (RSGs) are cool and evolved massive stars exhibiting enhanced mass loss compared to their main sequence phase, affecting their evolution and fate. However, the theory of the wind-driving mechanism is not well-established and…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
We investigate the mass loss of highly evolved, low- and intermediate mass stars and stellar samples with subsolar metallicity. We give a qualitative as well as quantitative description which can be applied to LMC/SMC-type stellar…
We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an…
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…