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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2406.04213 |
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| author | Gondhalekar, Yash Chies-Santos, Ana L. de Souza, Rafael S. Queiroz, Carolina Lopes, Amanda R. Ferrari, Fabricio Azevedo, Gabriel M. Monteiro-Pereira, Hellen Overzier, Roderik Castelli, Analía V. Smith Jaffé, Yara L. Haack, Rodrigo F. Rahna, P. T. Shen, Shiyin Mu, Zihao Lima-Dias, Ciria Barbosa, Carlos E. Schwarz, Gustavo B. Oliveira Riffel, Rogério Jimenez-Teja, Yolanda Grossi, Marco de Oliveira, Claudia L. Mendes Schoenell, William Ribeiro, Thiago Kanaan, Antonio |
| author_facet | Gondhalekar, Yash Chies-Santos, Ana L. de Souza, Rafael S. Queiroz, Carolina Lopes, Amanda R. Ferrari, Fabricio Azevedo, Gabriel M. Monteiro-Pereira, Hellen Overzier, Roderik Castelli, Analía V. Smith Jaffé, Yara L. Haack, Rodrigo F. Rahna, P. T. Shen, Shiyin Mu, Zihao Lima-Dias, Ciria Barbosa, Carlos E. Schwarz, Gustavo B. Oliveira Riffel, Rogério Jimenez-Teja, Yolanda Grossi, Marco de Oliveira, Claudia L. Mendes Schoenell, William Ribeiro, Thiago Kanaan, Antonio |
| contents | We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D Sérsic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large datasets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: A self-supervised visual identification aid Gondhalekar, Yash Chies-Santos, Ana L. de Souza, Rafael S. Queiroz, Carolina Lopes, Amanda R. Ferrari, Fabricio Azevedo, Gabriel M. Monteiro-Pereira, Hellen Overzier, Roderik Castelli, Analía V. Smith Jaffé, Yara L. Haack, Rodrigo F. Rahna, P. T. Shen, Shiyin Mu, Zihao Lima-Dias, Ciria Barbosa, Carlos E. Schwarz, Gustavo B. Oliveira Riffel, Rogério Jimenez-Teja, Yolanda Grossi, Marco de Oliveira, Claudia L. Mendes Schoenell, William Ribeiro, Thiago Kanaan, Antonio Astrophysics of Galaxies We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D Sérsic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large datasets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys. |
| title | Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: A self-supervised visual identification aid |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2406.04213 |