Related papers: Stellar flare detection in XMM-Newton with gradien…

Recent developments in computational power and machine learning techniques motivate their use in many different astrophysical research areas. Consequently, many machine learning models have been trained to classify exoplanet transit signals…

We explore methods for the identification of stellar flare events in irregularly sampled data of ground-based time domain surveys. In particular, we describe a new technique for identifying flaring stars, which we have implemented in a…

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and…

We carry out classification of 4330 X-ray sources in the 2XMMi-DR3 catalog. They are selected under the requirement of being a point source with multiple XMM-Newton observations and at least one detection with the signal-to-noise ratio…

The latest $\textit{Fermi}$-LAT gamma-ray catalog, 4FGL-DR3, presents a large fraction of sources without clear association to known counterparts, i.e., unidentified sources (unIDs). In this paper, we aim to classify them using machine…

We present the first rates of flares from M dwarf stars in both red optical and near infrared (NIR) filters. We have studied ~50,000 M dwarfs from the SDSS Stripe 82 area, and 1,321 M dwarfs from the 2MASS Calibration Scan Point Source…

Neutrinos generated during solar flares remain elusive. However, after $50$ years of discussion and search, the potential knowledge unleashed by their discovery keeps the search crucial. Neutrinos associated with solar flares provide…

XMM-Newton has produced an extensive X-ray source catalogue in which the standard pipeline determines the variability of sufficiently bright sources through chi-square and fractional variability tests. Faint sources, however, are not…

We present the results of a search of the XMM-Newton public data archive for stellar X-ray flares. We find eight flaring sources for which we identify 7 optical counterparts. Three of these sources have distance estimates which allow us to…

The prediction of solar flares is of practical and scientific interest; however, many machine learning methods used for this prediction task do not provide the physical explanations behind a model's performance. We made use of two recently…

Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…

We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGL unassociated sources. Characterization and classification of these sources allows for more complete population studies of the high-energy sky. Most of these X-ray…

Context. Large, high-dimensional astronomical surveys require efficient data analysis. Automatic fitting of lightcurve variability and machine learning may assist in identification of sources including candidate quasars. Aims. We aim to…

With machine learning entering into the awareness of the heliophysics community, solar flare prediction has become a topic of increased interest. Although machine learning models have advanced with each successive publication, the input…

Stellar flares are an important aspect of magnetic activity -- both for stellar evolution and circumstellar habitability viewpoints - but automatically and accurately finding them is still a challenge to researchers in the Big Data era of…

We present the first detection of an X-ray flare from an ultracool dwarf of spectral class L. The event was identified in the EXTraS database of XMM-Newton variable sources, and its optical counterpart, J0331-27, was found through a…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to…

We introduce the New-ANGELS program, an XMM-Newton survey of $\sim7.2\rm~deg^2$ area around M 31, which aims to study the X-ray populations in M 31 disk and the X-ray emitting hot gas in the inner halo of M 31 up to 30 kpc. In this first…

(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…