An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 data
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2025
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| author | Quispe-Huaynasi, F. Roig, F. Holanda, N. Loaiza-Tacuri, V. Eleutério, Romualdo Pereira, C. B. Daflon, S. Placco, V. M. de Oliveira, R. Lopes Sestito, F. Humire, P. K. Fernandes, M. Borges Kanaan, A. Ribeiro, T. Schoenell, W. |
| author_facet | Quispe-Huaynasi, F. Roig, F. Holanda, N. Loaiza-Tacuri, V. Eleutério, Romualdo Pereira, C. B. Daflon, S. Placco, V. M. de Oliveira, R. Lopes Sestito, F. Humire, P. K. Fernandes, M. Borges Kanaan, A. Ribeiro, T. Schoenell, W. |
| contents | 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 ($\sim 19,000$) whose Spectral Energy Distribution (SED) 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 (CEMP) 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 (DA), WDs with neutral helium-dominated atmospheres (DB), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 data Quispe-Huaynasi, F. Roig, F. Holanda, N. Loaiza-Tacuri, V. Eleutério, Romualdo Pereira, C. B. Daflon, S. Placco, V. M. de Oliveira, R. Lopes Sestito, F. Humire, P. K. Fernandes, M. Borges Kanaan, A. Ribeiro, T. Schoenell, W. Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics 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 ($\sim 19,000$) whose Spectral Energy Distribution (SED) 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 (CEMP) 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 (DA), WDs with neutral helium-dominated atmospheres (DB), 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. |
| title | An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 data |
| topic | Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2504.18491 |