GALAXY CRUISE: Deep Insights into Interacting Galaxies in the Local Universe

TL;DR

GALAXY CRUISE is a citizen science project analyzing deep imaging data to better understand galaxy interactions, mergers, and their effects on black holes and star formation, providing valuable classifications for future research.

Contribution

This study introduces GALAXY CRUISE, a large-scale citizen science effort that improves galaxy interaction identification and insights into galaxy evolution using HSC-SSP data.

Findings

Interacting galaxies show enhanced black hole activity and star formation.

Classification accuracy surpasses previous studies with shallower data.

Mass growth rates align with stellar mass function evolution.

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 have launched a community science project, GALAXY CRUISE, in 2019 and collected over 2 million independent classifications of 20,686 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 merger. 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 2nd season of GALAXY CRUISE is currently under way 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.