Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: A self-supervised visual identification aid
arxiv(2024)
Abstract
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 ∼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.
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