Related papers: Machine learning based stellar classification with…
High-precision photometric standard stars play a key role in enabling accurate photometric calibration and advancing various fields of astronomy. However, due to limitations in calibration methods and the limited availability and underuse…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies…
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…
Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from…
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 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…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These…
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…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very…
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques…
The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for…
We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic…
Modern large-scale photometric surveys have provided us with multi-band photometries of billions of stars. Determining the stellar atmospheric parameters, such as the effective temperature (\teff) and metallicities (\feh), absolute…
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…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…