Related papers: Variability search in M 31 using Principal Compone…
A complete periodic star extraction and classification scheme is set up and tested with the Hipparcos catalogue. The efficiency of each step is derived by comparing the results with prior knowledge coming from the catalogue or from the…
In the era of big data, reducing data dimensionality is critical in many areas of science. Widely used Principal Component Analysis (PCA) addresses this problem by computing a low dimensional data embedding that maximally explain variance…
Intensive monitoring of low-extinction windows towards the galactic bulge has provided in the last years valuable information for studies about the dynamics, kinematics and formation history of this part of the galaxy, mainly by…
We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing ADI and LOCI for increasing the contrast achievable next to a bright star. The stellar PSF is…
Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likelihood solution for the model is an eigenvalue problem on the…
Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. In this paper, we consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal.…
Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and thus, various robust PCA methods have been proposed.…
In this paper we present the WeCAPP variable star catalogue towards the bulge of M31. The observations of the WeCAPP microlensing survey (optical R and I bands) during three years (2000-2003) result in a database with unprecedented time…
With the development of modern technologies such as IFUs, it is possible to obtain data cubes in which one produces images with spectral resolution. To extract information from them can be quite complex, and hence the development of new…
Utilizing high-cadence and continuous g- and r-band data over three nights acquired from the 3.6-meter Canada France Hawaii Telescope (CFHT) aimed to find short-duration microlensing events, we conduct a systematic search for variables,…
Insights on stellar surface large-scale magnetic field topologies are usually drawn by applying Zeeman-Doppler-Imaging (ZDI) to the observed spectropolarimetric time series. However, ZDI requires experience for reliable results to be…
In many scientific disciplines, the features of interest cannot be observed directly, so must instead be inferred from observed behaviour. Latent variable analyses are increasingly employed to systematise these inferences, and Principal…
Principal component analysis (PCA) is a fundamental tool for analyzing multivariate data. Here the focus is on dimension reduction to the principal subspace, characterized by its projection matrix. The classical principal subspace can be…
Understanding the morphology of galaxies is a critical aspect of astrophysics research, providing insight into the formation, evolution, and physical properties of these vast cosmic structures. Various observational and computational…
Principal component analysis (PCA) is a popular dimension reduction technique often used to visualize high-dimensional data structures. In genomics, this can involve millions of variables, but only tens to hundreds of observations.…
By digitizing astronomical photographic plates one may extract full information stored on them, something that could not be practically achieved with classical analogue methods. We are developing algorithms for variable objects search using…
We present a study aimed at understanding the physical phenomena underlying the formation and evolution of galaxies following a data-driven analysis of spectroscopic data based on the variance in a carefully selected sample. We apply…
Photometric measurements are prone to systematic errors presenting a challenge to low-amplitude variability detection. In search for a general-purpose variability detection technique able to recover a broad range of variability types…
We aim to construct an exceptionally deep (V ~< 27) catalog of variable objects in selected Galactic and extragalactic fields visited multiple times by the Hubble Space Telescope (HST). While HST observations of some of these fields were…
Principal component analysis (PCA) is often used to analyze multivariate data together with cluster analysis, which depends on the number of principal components used. It is therefore important to determine the number of significant…