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Masayuki Tanaka, Michitaro Koike, Sei’ichiro Naito, Junko Shibata, Kumiko Usuda-Sato, Hitoshi Yamaoka, Makoto Ando, Kei Ito, Umi Kobayashi, Yutaro Kofuji, Atsuki Kuwata, Suzuka Nakano, Rhythm Shimakawa, Ken-ichi Tadaki, Suguru Takebayashi, Chie Tsuchiya, Tomofumi Umemoto, Connor Bottrell, Galaxy Cruise: Deep Insights into Interacting Galaxies in the Local Universe, Publications of the Astronomical Society of Japan, Volume 75, Issue 5, October 2023, Pages 986–1010, https://doi.org/10.1093/pasj/psad055
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Abstract
We present the first results from GALAXY CRUISE, a community (or citizen) science project based on data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). The current paradigm of galaxy evolution suggests that galaxies grow hierarchically via mergers, but our observational understanding of the role of mergers is still limited. The data from HSC-SSP are ideally suited to improve our understanding with improved identifications of interacting galaxies thanks to the superb depth and image quality of HSC-SSP. We launched a community science project, GALAXY CRUISE, in 2019 and have collected over two million independent classifications of 20686 galaxies at z < 0.2. We first characterize the accuracy of the participants’ classifications and demonstrate that it surpasses previous studies based on shallower imaging data. We then investigate various aspects of interacting galaxies in detail. We show that there is a clear sign of enhanced activities of super-massive black holes and star formation in interacting galaxies compared to those in isolated galaxies. The enhancement seems particularly strong for galaxies undergoing violent mergers. We also show that the mass growth rate inferred from our results is roughly consistent with the observed evolution of the stellar mass function. The second season of GALAXY CRUISE is currently underway and we conclude with future prospects. We make the morphological classification catalog used in this paper publicly available at the GALAXY CRUISE website, which will be particularly useful for machine-learning applications.