An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data
Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify candidates for spectroscopic follow-up using data from the Southern Photometric Local Universe Survey (S-PLUS). First, using an anomaly detection technique based on an autoencoder model, we select a large sample of objects (∼19,000) whose Spectral Energy Distribution is not well reconstructed by the model after training it on a well-behaved star sample. Then, we apply the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm to the 66 color measurements from S-PLUS, complemented by information from the SIMBAD database, to identify stellar populations. Our analysis reveals 69 carbon-rich star candidates that, based on their spatial and kinematic characteristics, may belong to the CH or carbon-enhanced metal-poor categories. Among these chemically peculiar candidates, we identify four as likely carbon dwarf stars. We show that it is feasible to identify three primary white-dwarf (WD) populations: WDs with hydrogen-dominated atmospheres, WDs with neutral helium-dominated atmospheres, and the WDs main sequence binaries (WD + MS). Furthermore, by using eROSITA X-ray data, we also highlight the identification of candidates for very active low-mass stars. Finally, we identified a large number of binary systems using the autoencoder model, but did not observe a clear association between the overdensities in the t-SNE map and their orbital properties.
Item Type | Article |
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Additional information | © 2025 The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/ |
Keywords | white dwarf stars, low mass stars, carbon stars, chemically peculiar stars, binary stars |
Date Deposited | 27 May 2025 08:11 |
Last Modified | 02 Jun 2025 03:00 |