Related papers: Random Forest Classification of Stars in the Galac…

The goals of this work are to develop a new method to separate early and late type stellar components of a dense stellar cluster based on narrow band filters, to apply it to the central parsec of the GC, and to conduct a population analysis…

With the availability of multi-object spectrometers and the designing \& running of some large scale sky surveys, we are obtaining massive spectra. Therefore, it becomes more and more important to deal with the massive spectral data…

The second mission of the NASA Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our…

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…

Knowing the metallicity distribution of stars in the Galactic Centre has important implications for the formation history of the Milky Way nuclear star cluster. However, this distribution is not well known, and is currently based on a small…

Variable stars play a very important role in our understanding of the Milky Way and the universe. In recent years, many survey projects have generated a large amount of photometric data, necessitating classifiers that can quickly identify…

(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a…

We present new high angular resolution near-infrared spectroscopic observations of the nuclear star cluster surrounding the Milky Way's central supermassive black hole. Using the integral-field spectrograph OSIRIS on Keck II behind the…

The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation…

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…

In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color…

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…

The presence of young massive stars in the Galactic Centre (GC) raises questions about star formation near the black hole Sagittarius A* (Sgr A*). Additionally, the initial mass function (IMF) in this region appears different from the…

We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

Context: Given the current big data era in Astronomy, machine learning based methods have being applied over the last years to identify or classify objects like quasars, galaxies and stars from full sky photometric surveys. Aims: Here we…

We use the Random Forest (RF) algorithm to develop a tool for automated activity classification of galaxies into 5 different classes: Star-forming (SF), AGN, LINER, Composite, and Passive. We train the algorithm on a combination of mid-IR…

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…

In a dynamically relaxed cluster around a massive black hole a dense stellar cusp of old stars is expected to form. Previous observations showed a relative paucity of red giant stars within the central 0.5 pc in the Galactic Center. By…

We present new HST I-band images of a sample of 77 nearby, late-type spiral galaxies with low inclination. The main purpose of this catalog is to study the frequency and properties of nuclear star clusters. In 59 galaxies of our sample, we…