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

A few percent of all stars are variable, yet over 90% of variables brighter than 12 magnitude have not been discovered yet. There is a need for an all sky search and for the early detection of any unexpected events: optical flashes from…

Model-independent analysis (MIA) methods are generally useful for analysing complex systems in which relationships between the observables are non-trivial and noise is present. Principle Component Analysis (PCA) is one of MIA methods…

Sparse principal component analysis (sparse PCA) is a widely used technique for dimensionality reduction in multivariate analysis, addressing two key limitations of standard PCA. First, sparse PCA can be implemented in high-dimensional low…

We describe techniques to characterise the light-curves of regular variable stars by applying principal component analysis (PCA) to a training set of high quality data, and to fit the resulting light-curve templates to sparse and noisy…

Determination of cosmological parameters is a major goal in cosmology at present. The availability of improved data sets necessitates the development of novel statistical tools to interpret the inference from a cosmological model. In this…

We have monitored a 3 deg2 area toward Serpens Main in the Pan-STARRS1 r, i, and z bands from 2016 April to September. Light curves of more than 11,000 stars in each band were obtained, and 143 variables have been identified. Among those,…

Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, the…

We describe an ongoing variable star search program and present the first reduced results of a search in a 19 square degree (4.4\circle x 4.4\circle) field centered on J2000 {\alpha} = 22:03:24, {\delta} = +18:54:32. The search was carried…

Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating…

This project is a massive near-infrared (NIR) search for variable stars in highly reddened and obscured open cluster (OC) fields projected on regions of the Galactic bulge and disk. The search is performed using photometric NIR data in the…

Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2D images as large as a few hundred pixels in each direction. Here we introduce an algorithm that efficiently and accurately performs principal…

We have conducted a variability survey of the local group galaxy M33 using g', r', and i' observations from 27 nights spanning 17 months made with the MegaPrime/MegaCam instrument on the 3.6 m CFHT telescope. We identify more than 36000…

A principal component analysis (PCA) of clean microcalorimeter pulse records can be a first step beyond statistically optimal linear filtering of pulses towards a fully non-linear analysis. For PCA to be practical on spectrometers with…

Varying fundamental constants (VFC) [e.g., the fine-structure constant, $\alpha_{\rm EM}$] can arise in numerous extended cosmologies. Through their effect on the decoupling of baryons and photons during last scattering and reionisation,…

[Abridged] The central field of the Andromeda galaxy (M 31) has been monitored, using the Chandra HRC-I detector (about 0.1-10 keV energy range) from 2006 to 2012 with the main aim to detect X-rays from optical novae. We present a…

We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the…

Obtain time-series photometry of the very crowded central regions of Galactic globular clusters with better angular resolution than previously achieved with conventional CCDs on ground-based telescopes to complete, or improve, the census of…

Principal Component Analysis (PCA) is the most common nonparametric method for estimating the volatility structure of Gaussian interest rate models. One major difficulty in the estimation of these models is the fact that forward rate curves…

We observed two fields near M32 with the ACS/HRC on board the Hubble Space Telescope, located at distances of about 1.8' and 5.4' (hereafter F1 and F2, respectively) from the center of M32. To obtain a very detailed and deep color-magnitude…

A photometric study of variable stars in the field of old open cluster NGC 188 is discussed. Observations were carried out in two bands R and I for 5513 stars up to R = 17 mag in the field of 1.5 x 1.5 grad. around the cluster. The…