A simple linear model to aid in analyses of the Beta Pictoris moving group
Josina O. do Nascimento, Valmir C. Barbosa

TL;DR
This paper develops a simple four-dimensional linear model using PCA to analyze object membership in the Beta Pictoris moving group, aiding in the classification of stellar members based on galactic velocities and Gaia data.
Contribution
The paper introduces a novel PCA-based linear model for efficiently analyzing and classifying members of the Beta Pictoris moving group using high-dimensional data.
Findings
The model effectively clusters bona fide and candidate members around a principal component.
Flagging outliers based on distances to PC 1' is consistent with data quality.
The approach can be extended to other young stellar groups.
Abstract
We build a four-dimensional linear model of object membership in the Beta Pictoris moving group (BPMG), using two nested applications of Principal Component Analysis (PCA) to high-quality data on about 1.5 million objects. These data contain the objects' galactic space velocities and also their Gaia magnitudes. Through PCA, they ultimately result in a four-dimensional straight line, referred to as PC , about which both the bona fide members used to obtain the straight line and the candidate members used to test the model congregate at generally small distances. Our bona fide members come from a recent, Gaia DR2-based compilation. Most candidate members are from a compilation from 2017. Using a standard procedure to flag groups of outliers in data sets, we argue that flagging the few possible outliers we identified on account of distances to PC is consistent with the nature…
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Taxonomy
TopicsIsotope Analysis in Ecology · Gamma-ray bursts and supernovae · Stellar, planetary, and galactic studies
