SIMBAD references

2018MNRAS.476.2117R - Mon. Not. R. Astron. Soc., 476, 2117-2136 (2018/May-2)

Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars.

REIS I., POZNANSKI D., BARON D., ZASOWSKI G. and SHAHAF S.

Abstract (from CDS):

In this work, we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the data set, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the data set for objects allows us to find objects that are impossible to find using their best-fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the data set, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data.

Abstract Copyright: © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): methods: data analysis - stars: general - stars: peculiar

VizieR on-line data: <Available at CDS (J/MNRAS/476/2117): apogeenn.dat distance.dat tsnecoor.dat>

Simbad objects: 11

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2021.05.07-12:52:09

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