Related papers: Variability search in M 31 using Principal Compone…

Variable stars in the compact elliptical galaxy M32 are identified, using three epochs of photometry from the Spitzer Space Telescope at 3.6 and 4.5 $\mu$m, separated by 32 to 381 days. We present a high-fidelity catalogue of sources…

A radial velocity (RV) survey to detect central stars in binary systems was carried out between 2002 and 2004. De Marco et al. (2004) reported that 10 out of 11 monitored stars exhibited strong RV variability, but periods were not detected.…

This study presents a scalable data-driven algorithm designed to efficiently address the challenging problem of reachability analysis. Analysis of cyber-physical systems (CPS) relies typically on parametric physical models of dynamical…

Principal Component Analysis (PCA) is a commonly used tool for dimension reduction in analyzing high dimensional data; Multilinear Principal Component Analysis (MPCA) has the potential to serve the similar function for analyzing tensor…

This study aims to assess the properties and classification of 55 variable stars in Scutum, little studied since their discovery and reported in the Information Bulletin on Variable Stars (IBVS) 985 and update. Using data from previous…

The ESA Gaia mission provides a unique time-domain survey for more than 1.6 billion sources with G ~ 21 mag. We showcase stellar variability across the Galactic colour-absolute magnitude diagram (CaMD), focusing on pulsating, eruptive, and…

Deep B,V time-series photometry obtained with the ESO Very Large Telescope has been used to identify variable stars in the dwarf irregular galaxy NGC 6822. We surveyed a 6.8x6.8 arcmin area of the galaxy and detected a total number of 390…

Principal Component Analysis (PCA) is widely used for dimensionality reduction and data analysis. However, PCA results are adversely affected by outliers often observed in real-world data. Existing robust PCA methods are often…

We present the analysis of 26 nights of V and I time-series observations from 2011 and 2012 of the globular cluster M 30 (NGC 7099). We used our data to search for variable stars in this cluster and refine the periods of known variables; we…

Functional data typically contains amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, although it may contain valuable information. In this note, we…

Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In…

The Asteroid Terrestrial-impact Last Alert System (ATLAS) carries out its primary planetary defense mission by surveying about 13000 deg^2 at least four times per night. The resulting data set is useful for the discovery of variable stars…

CONTEXT: The unparalleled characteristics of Gaia photometry make it an excellent choice to study stellar variability. AIMS: To measure the phot. dispersion in G+G_BP+G_RP of the 145 677 450 Gaia DR3 5-parameter sources with G <= 17 mag and…

Here, we present a machine vision approach, combining a VAE framework with PCA, to decipher galaxy images. Using mock gri-band images from the EAGLE simulation, the VAE finds that around 35 features are needed to describe the images. Adding…

The idea of representation has been used in various fields of study from data analysis to political science. In this paper, we define representativeness and describe a method to isolate data points that can represent the entire data set.…

We used four-year baseline HST/WFC3 IR observations of the Galactic Centre in the F153M band (1.53 micron) to identify variable stars in the central ~2.3'x2.3' field. We classified 3845 long-term (periods from months to years) and 76…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

Sparse versions of principal component analysis (PCA) have imposed themselves as simple, yet powerful ways of selecting relevant features of high-dimensional data in an unsupervised manner. However, when several sparse principal components…

Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling. It is an unsupervised learning technique that identifies a suitable linear subspace for the input…

We report on the results of a long time photometric monitoring of the two metal poor Galactic globular clusters M22 and IC4499 searching for long period variables (LPVs) on the upper giant branch. We detected 22 new LPVs in the field of M22…