Galaxy mergers in Subaru HSC-SSP: A deep representation learning approach for identification, and the role of environment on merger incidence

• Published in: Astronomy and astrophysics (Berlin) 2023-11, Vol.679, p.A142
• Main Authors: Omori, Kiyoaki Christopher, Bottrell, Connor, Walmsley, Mike, Yesuf, Hassen M., Goulding, Andy D., Ding, Xuheng, Popping, Gergö, Silverman, John D., Takeuchi, Tsutomu T., Toba, Yoshiki
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Context; Deep learning; Density; Galactic evolution; Galaxy mergers & collisions; Image classification; Machine learning; Mass ratios; Model accuracy; Morphology; Representations; Simulation; Stars & galaxies; Stellar mass; Synthetic data
• ISSN: 0004-6361; 1432-0746
• DOI: 10.1051/0004-6361/202346743

Context. Galaxy mergers and interactions are an important process within the context of galaxy evolution, however, there is still no definitive method which identifies pure and complete merger samples is still not definitive. A method for creating such a merger sample is required so that studies can be conducted to deepen our understanding of the merger process and its impact on galaxy evolution. Aims. In this work, we take a deep-learning-based approach for galaxy merger identification in Subaru HSC-SSP, using deep representation learning and fine-tuning, with the aim of creating a pure and complete merger sample within the HSC-SSP survey. We can use this merger sample to conduct studies on how mergers affect galaxy evolution. Methods. We used Zoobot, a deep learning representation learning model pretrained on citizen science votes on Galaxy Zoo DeCALS images. We fine-tuned Zoobot for the purpose of merger classification of images of SDSS and GAMA galaxies in HSC-SSP public data release 3. Fine-tuning was done using ∼1200 synthetic HSC-SSP images of galaxies from the TNG simulation. We then found merger probabilities on observed HSC images using the fine-tuned model. Using our merger probabilities, we examined the relationship between merger activity and environment. Results. We find that our fine-tuned model returns an accuracy on the synthetic validation data of ∼76%. This number is comparable to those of previous studies in which convolutional neural networks were trained with simulation images, but with our work requiring a far smaller number of training samples. For our synthetic data, our model is able to achieve completeness and precision values of ∼80%. In addition, our model is able to correctly classify both mergers and non-mergers of diverse morphologies and structures, including those at various stages and mass ratios, while distinguishing between projections and merger pairs. For the relation between galaxy mergers and environment, we find two distinct trends. Using stellar mass overdensity estimates for TNG simulations and observations using SDSS and GAMA, we find that galaxies with higher merger scores favor lower density environments on scales of 0.5 to 8 h −1 Mpc. However, below these scales in the simulations, we find that galaxies with higher merger scores favor higher density environments. Conclusions. We fine-tuned a citizen-science trained deep representation learning model for purpose of merger galaxy classification in HSC-SSP, and