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

Takumi S. Tanaka*, Rhythm Shimakawa, Kazuhiro Shimasaku, Yoshiki Toba, Nobunari Kashikawa, Masayuki Tanaka, Akio K. Inoue

*この研究の対応する著者

研究成果: Article査読

4 被引用数 (Scopus)

抄録

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, 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.

本文言語English
ページ(範囲)1-23
ページ数23
ジャーナルPublications of the Astronomical Society of Japan
74
1
DOI
出版ステータスPublished - 2022 2月 1

ASJC Scopus subject areas

  • 天文学と天体物理学
  • 宇宙惑星科学

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