Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data

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Gutierrez-Soto, L. A. ; Lopes de Oliveira, R. ; Akras, S. ; Goncalves, D. R. ; Lomeli-Nunez, L. F. ; Mendes de Oliveira, C. ; Telles, E. ; Alvarez-Candal, A. ; Borges Fernandes, M. ; Daflon, S. ; Ferreira Lopes, C. E. ; Grossi, M. ; Hazarika, D. ; Humire, P. K. ; Lima-Dias, C. ; Lopes, A. R. ; Nilo Castellon, J. L. ; Panda, S. ; Kanaan, A. ; Ribeiro, T. ; Schoenell, W.

• Tipo de documento: Artigo Científico
• Fonte: Astronomy & Astrophysics; v. 695, p. 23-pg., 2025-03-14.

Resumo

Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify H alpha excess point source candidates in the southern sky. This approach combines photometric data from 12 S-PLUS filters with machine learning techniques to improve source classification and advance our understanding of H alpha-related phenomena. Aims. Our goal is to enhance the classification of H alpha excess point sources by distinguishing between Galactic and extragalactic objects, particularly those with redshifted emission lines, and to identify sources where the H alpha excess is associated with variability phenomena, such as short-period RR Lyrae stars. Methods. We selected H alpha excess candidates using the (r - J0660) versus (r - i) colour-colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). For the MS sample, dimensionality reduction was achieved using UMAP, followed by HDBSCAN clustering. We refined this by incorporating infrared data, which improved the separation of source types. A random forest model was then trained on the clustering results to identify key colour features for the classification of H alpha excess sources. New effective colour-colour diagrams were constructed by combining data from S-PLUS MS and infrared data. These diagrams, alongside tentative colour criteria, offer a preliminary classification of H alpha excess sources without the need for complex algorithms. Results. Combining multi-wavelength photometric data with machine learning techniques significantly improved the classification of H alpha excess sources. We identified 6956 sources with an excess in the J0660 filter, and cross-matching with SIMBAD allowed us to explore the types of objects present in our catalogue, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, variable stars, and extragalactic sources such as Quasi-Stellar Objects (QSOs), Active Galactic Nuclei (AGN), and galaxies. The cross-match also revealed X-ray sources, transients, and other peculiar objects. Using S-PLUS colours and machine learning, we successfully separated RR Lyrae stars from other Galactic stars and from extragalactic objects. Additionally, we achieved a clear separation between Galactic and extragalactic sources. However, distinguishing cataclysmic variables from QSOs at specific redshifts remained challenging. Incorporating infrared data refined the classification, enabling us to separate Galactic from extragalactic sources and to distinguish cataclysmic variables from QSOs. The Random Forest model, trained on HDBSCAN results, highlighted key colour features that distinguish the different classes of H alpha excess sources, providing a robust framework for future studies, such as follow-up spectroscopy. (AU)

• Processo FAPESP: 19/26412-0 - Desenvolvimento de ferramentas para o banco de dados e para análise de dados do telescópio T80-sul
• Beneficiário: Luis Angel Gutierrez Soto
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 23/14272-4 - Distribuição de energia espectral da galáxia NGC253, uma abordagem de alta resolução com vários comprimentos de onda
• Beneficiário: Pedro Kurmi Humire Rodríguez
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 20/00457-4 - Identificação de Emissores Halpha usando o S-PLUS
• Beneficiário: Claudia Lucia Mendes de Oliveira
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Brasil