Where's Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

TL;DR

This paper introduces Swimmy, a deep-learning anomaly detection method applied to Subaru Hyper Suprime-Cam data, effectively identifying rare and unique galaxy populations without labeled training data, and revealing their physical and morphological properties.

Contribution

The paper presents a novel deep anomaly detection technique tailored for wide-field galaxy imaging data, successfully identifying rare objects like XELGs and quasars without prior labels.

Findings

Successfully detected 60-70% of quasars and 60% of XELGs as anomalies

Identified galaxies with unique morphological features

Demonstrated deep anomaly detection as an effective tool for discovering rare astronomical objects

Abstract

We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored ($grizy$) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as "outliers" from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected 60-70% of the quasars and 60% of the XELGs without labeled training data. In reference to the spectral information of local galaxies at $z=$0.05-0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, a deep anomaly detection is an effective tool that can search rare objects, and ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.