Related papers: A simple linear model to aid in analyses of the Be…
We use the Gaia DR2 catalog to improve the astrometric accuracy of about 1.7 billion objects in Pan-STARRS1 Data Release 2 (PS1 DR2). We also obtain proper motions for these PS1 objects. The cross match between Gaia and PS1 reveals…
The kinematics of about 40 single stars belonging to the $\beta$ Pictoris moving group is studied. The age of the $\beta$ Pictoris moving group is estimated from these stars with ground-based line-of-sight velocity determinations by two…
Beta Pictoris is an A-type star hosting a complex planetary system with two massive gas giants and a prominent debris disk. Variable absorption lines in its stellar spectrum have been interpreted as signatures of exocomets (comet-like…
We model the detectability of exoplanets around stars in the Beta Pic Moving Group (BPMG) using the Gemini Planet Imager (GPI), a coronagraphic instrument designed to detect companions by imaging. Members of the BPMG are considered…
The Gaia mission is expected to yield the detection of several thousands of exoplanets, perhaps at least doubling the number of known exoplanets. Although the harvest is expected to occur when the astrometric time series will be published…
We combine Gaia DR1, PS1, SDSS and 2MASS astrometry to measure proper motions for 350 million sources across three-fourths of the sky down to a magnitude of $m_r\sim20$\,. Using positions of galaxies from PS1, we build a common reference…
Principal component analysis (PCA) is a commonly used pattern analysis method that maps high-dimensional data into a lower-dimensional space maximizing the data variance, that results in the promotion of separability of data. Inspired by…
We study principal component analysis (PCA), where given a dataset in $\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ that approximately maximizes the variance of the distribution after being projected along $v$.…
Absolute astrometry with Gaia is expected to detect and characterize the orbits of thousands of exoplanets in the coming years. A tool, GaiaPMEX, was recently developed to characterize multiple systems based on Gaia-only data, and, when…
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…
Principal Component Analysis (PCA) is a well-known technique used to decorrelate a set of vectors. It has been applied to explore the star formation history of galaxies or to determine distances of mass-lossing stars. Here we apply PCA to…
Three-dimensional (3D) spectroscopy techniques are becoming more and more popular, producing an increasing number of large data cubes. The challenge of extracting information from these cubes requires the development of new techniques for…
We present the construction and describe the properties of the Padova-Millennium Galaxy and Group Catalogue (PM2GC), a galaxy catalogue representative of the general field population in the local Universe. We characterize galaxy…
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for grounding large language models in external knowledge sources, improving the precision of agents responses. However, high-dimensional language model embeddings,…
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…
Principal component analysis (PCA) is a classical feature extraction method, but it may be adversely affected by outliers, resulting in inaccurate learning of the projection matrix. This paper proposes a robust method to estimate both the…
In this paper, we propose a novel approach named by Discriminative Principal Component Analysis which is abbreviated as Discriminative PCA in order to enhance separability of PCA by Linear Discriminant Analysis (LDA). The proposed method…
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…
A variety of interstellar complex organic molecules (COMs) have been detected in various physical conditions. However, in the protostellar and protoplanetary environments, their complex kinematics make line profiles blend each other and the…
Searching for departures from general relativity (GR) in more than one post-Newtonian (PN) phasing coefficients, called a \emph{multi-parameter test}, is known to be ineffective given the sensitivity of the present generation of…