Related papers: Random Forest Classification of Stars in the Galac…
In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine…
Massive clusters in our Galaxy are an ideal testbed to investigate the properties and evolution of high mass stars. They provide statistically significant samples of massive stars of uniform ages. To accurately determine the intrinsic…
Massive stars are mainly found in stellar associations. These massive star clusters occur in the heart of giant molecular clouds. The strong stellar wind activity in these objects generates large bubbles and induces collective effects that…
A key tool astronomers have to investigate the nature of extragalactic transients is their position on their host galaxies. Galactocentric offsets, enclosed fluxes and the fraction of light statistic are widely used at different wavelengths…
We use machine learning to classify galaxies according to their HI content, based on both their optical photometry and environmental properties. The data used for our analyses are the outputs in the range $z = 0-1$ from MUFASA cosmological…
Classification of intermediate redshift ($z$ = 0.3--0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGN), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was…
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 present our photometric method, which combines Subaru/HSC $NB515$, g, and i band filters to distinguish giant stars in Local Group galaxies from Milky Way dwarf contamination. The $NB515$ filter is a narrow-band filter that covers the…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
The Milky Way's nuclear stellar disc (NSD) and nuclear star cluster (NSC) are the main features of the Galactic centre. Nevertheless, their observation is hampered by the extreme source crowding and high extinction. Hence, their relation…
Mass ($M$) and luminosity ($L$) are fundamental parameters of stars but can only be measured indirectly. Typically, effective temperature ($T_{\rm eff}$), surface gravity (${\rm log}\ g$) and metallicity ([M/H]) are derived from stellar…
Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. One of these classification and regression…
The nuclear star cluster of the Milky Way is a unique target in the Universe. Contrary to extragalactic nuclear star clusters, using current technology it can be resolved into tens of thousands of individual stars. This allows us to study…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that…
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…
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…
Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Being able to automatically classify these images is a…
This is the second of three papers that search for the predicted stellar cusp around the Milky Way's central black hole, Sagittarius A*, with new data and methods. We aim to infer the distribution of the faintest stellar population…
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…