相关论文: Imbalanced learning for RR Lyrae stars

In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently…

The Great Observatories All-sky LIRG Survey (GOALS) consists of a complete sample of 202 Luminous Infrared Galaxies (LIRGs) selected from the IRAS Revised Bright Galaxy Sample (RBGS). The galaxies span the full range of interaction stages,…

The possible existence of stellar halos in low-mass galaxies is being intensely discussed nowadays after some recent discoveries of stars located in the outskirts of dwarf galaxies of the Local Group. RR Lyrae stars can be used to identify…

We study the variable star content of the globular cluster M53 to compute the physical parameters of the constituting stars and the distance of the cluster. Covering two adjacent seasons in 2007 and 2008, new photometric data are gathered…

Convex hulls are fundamental geometric tools used in a number of algorithms. This paper presents a fast, simple to implement and robust Smart Convex Hull (S-CH) algorithm for computing the convex hull of a set of points in E3. This…

We present a Bayesian analysis of the distances to 15,040 Large Magellanic Cloud (LMC) RR Lyrae stars using $V$- and $I$-band light curves from the Optical Gravitational Lensing Experiment, in combination with new $z$-band observations from…

This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys:…

Combined observations from UV to IR wavelengths are necessary to fully account for the star-formation in galaxy clusters. Low mass (log M/Msun<10) galaxies are typically not individualy detected, particularly at higher redshifts (z~1-2)…

We present the catalog of RR Lyrae stars from 2.4 square degrees of central parts of the Small Magellanic Cloud (SMC). The photometric data were collected during four years of the OGLE-II microlensing survey. Photometry of each star was…

Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…

We identify 51 blue horizontal branch (BHB) stars, 12 possible BHB stars and 58 RR Lyrae stars in Anticentre fields. Their selection does not depend on their kinematics. Light curves and ephemerides are given for 7 previously unknown RR…

We present VIVACE, the VIrac VAriable Classification Ensemble, a catalogue of variable stars extracted from an automated classification pipeline for the Vista Variables in the V\'ia L\'actea (VVV) infrared survey of the Galactic bar/bulge…

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…

We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital…

Variational autoencoders are among the most popular methods for distilling low-dimensional structure from high-dimensional data, making them increasingly valuable as tools for data exploration and scientific discovery. However, unlike…

Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…

We present a careful and detailed light curve analysis of RR Lyrae stars in the Small Magellanic Cloud (SMC) discovered by the Optical Gravitational Lensing Experiment (OGLE) project. Out of 536 single mode RR Lyrae stars selected from the…

We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…

We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of…

Our study aims to recognize M-type stars which are classified as "UNKNOWN" due to bad quality in Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) DR5 V1. A binary nonlinear hashing algorithm based on Multi-Layer Pseudo…