Related papers: Structural properties and classification of variab…

The eighth part of the OGLE-III Catalog of Variable Stars (OIII-CVS) contains type II Cepheids in the Small Magellanic Cloud (SMC). The sample consists of 43 objects, including 17 BL Her, 17 W Vir and 9 RV Tau stars (first examples ever…

Finding overcomplete latent representations of data has applications in data analysis, signal processing, machine learning, theoretical neuroscience and many other fields. In an overcomplete representation, the number of latent features…

We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas…

Approximately four thousand light curves of red variable stars in the LMC were selected from the 2.3-years duration MOA database by a period analysis using the Phase Dispersion Minimization method. Their optical features (amplitudes,…

Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods…

Principal Component Analysis (PCA) and K-means constitute fundamental techniques in multivariate analysis. Although they are frequently applied independently or sequentially to cluster observations, the relationship between them, especially…

Although approaches to Independent Component Analysis (ICA) based on characteristic function seem theoretically elegant, they may suffer from implementational challenges because of numerical integration steps or selection of tuning…

Independent Component Analysis (ICA) is a fundamental unsupervised learning technique foruncovering latent structure in data by separating mixed signals into their independent sources. While substantial progress has been made in…

Detailed knowledge of the variability of classical Cepheids, in particular their modulations and mode composition, provides crucial insight into stellar structure and pulsation. However, tiny modulations of the dominant radial-mode…

We present a variability study of 4646 massive stars in the Small Magellanic Cloud (SMC) with known spectral types from the catalog of Bonanos et al. (2010) using the light curves from the OGLE-III database. The goal is to exploit the time…

Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement…

Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting…

Machine-learning (ML) algorithms will play a crucial role in studying the large datasets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with…

We present a methodology to discover outliers in catalogs of periodic light-curves. We use cross-correlation as measure of ``similarity'' between two individual light-curves and then classify light-curves with lowest average ``similarity''…

In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as…

The first results are presented of a four-year program dedicated to the CCD observations of Cepheids in the nearby galaxy IC 1613. Since the program was carried out with a relatively small telescope, the Dutch 0.9 m at ESO-La Silla, the…

Nonlinear independent component analysis (ICA) aims to uncover the true latent sources from their observable nonlinear mixtures. Despite its significance, the identifiability of nonlinear ICA is known to be impossible without additional…

In this paper, we apply a new statistical analysis technique, Mean Field approach to Bayesian Independent Component Analysis (MF-ICA), on galaxy spectral analysis. This algorithm can compress the stellar spectral library into a few…

One hundred and forty six long-period red variable stars in the Large Magellanic Cloud (LMC) from the three year MOA project database were analysed. A careful periodic analysis was performed on these stars and a catalogue of their…

We use the OGLE-II and OGLE-III data in conjunction with the 2MASS near-infrared (NIR) photometry to identify and study Miras and Semiregular Variables (SRVs) in the Large Magellanic Cloud. We found in total 3221 variables of both types,…