Related papers: Imbalanced learning for RR Lyrae stars

Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…

Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…

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…

Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional "Lost-in-Space" (LIS) algorithms often suffer from high computational overhead and sensitivity to sensor-induced…

In order to constrain the evolutionary history of the Milky Way, we hunt for faint RR Lyrae stars (RRLs) using Dark Energy Camera data from the High cadence Transient Survey (HiTS) and the Halo Outskirts With Variable Stars (HOWVAST)…

The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years,…

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically…

Iterative methods such as iterative closest point (ICP) for point cloud registration often suffer from bad local optimality (e.g. saddle points), due to the nature of nonconvex optimization. To address this fundamental challenge, in this…

Context: This paper presents new results from the ESO Large Programme Looking into the faintEst WIth MUSE (LEWIS). The sample consists of low-surface brightness (LSB) and ultra-diffuse galaxies (UDGs) located inside 0.4 R$_{vir}$ of the…

We study dust attenuation and stellar mass of $\rm z\sim 0.6$ star-forming galaxies using new SWIRE observations in IR and GALEX observations in UV. Two samples are selected from the SWIRE and GALEX source catalogs in the SWIRE/GALEX field…

In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest…

We show that a straightforward application of the predicted relation connecting the absolute K magnitude of fundamental RR Lyrae variables to their period and metal content, together with current evolutionary predictions on the mass and…

We employ a sample of 135,873 RR Lyrae stars (RRLs) with precise photometric-metallicity and distance estimates from the newly calibrated $P$--$\phi_{31}$--$R_{21}$--[Fe/H] and $Gaia$ $G$-band $P$--$R_{21}$--[Fe/H] absolute…

We present a comprehensive catalog of 2824 RR Lyrae stars (RRLs) residing in 115 Galactic globular clusters (GCs). Our catalog includes 1594 fundamental-mode (RRab), 824 first-overtone (RRc), and 28 double-mode (RRd) RRLs, as well as 378…

The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from…

This paper introduces a new type of regression methodology named as Convex-Area-Wise Linear Regression(CALR), which separates given datasets by disjoint convex areas and fits different linear regression models for different areas. This…

In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…

We develop a machine learning-based framework to predict the HI content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental parameters. We train the algorithm on z=0-2 outputs from the…

Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…

Context. Observational evidence has accumulated in the past years, showing that the Galactic bulge includes two populations, a metal poor and a metal rich one that, in addition to a different metallicity, show different alpha over iron…