Searching for previously unknown classes of objects in the AKARI-NEP Deep data with fuzzy logic SVM algorithm

機関リポジトリ

抄録

第4回「あかり」国際会議 (2017年10月17-20日. 東京大学), 文京区, 東京

The 4th AKARI International Conference: The Cosmic Wheel and the Legacy of the AKARI archive: from galaxies and stars to planets and life (October 17-20, 2017. The University of Tokyo), Bunkyo-ku, Tokyo, Japan

In this proceedings application of a fuzzy Support Vector Machine (FSVM) learning algorithm, to classify mid-infrared (MIR) sources from the AKARI NEP Deep field into three classes: stars, galaxies and AGNs, is presented. FSVM is an improved version of the classical SVM algorithm, incorporating measurement errors into the classification process; this is the first successful application of this algorithm in the astronomy. We created reliable catalogues of galaxies, stars and AGNs consisting of objects with MIR measurements, some of them with no optical counterparts. Some examples of identified objects are shown, among them O-rich and C-rich AGB stars.

形態: カラー図版あり

Physical characteristics: Original contains color illustrations

資料番号: AA1730026076

レポート番号: JAXA-SP-17-009E

収録刊行物

キーワード

詳細情報 詳細情報について

  • CRID
    1050011086257255680
  • NII論文ID
    120006827380
  • ISSN
    24332232
  • Web Site
    http://id.nii.ac.jp/1696/00003144/
  • 本文言語コード
    en
  • 資料種別
    conference paper
  • データソース種別
    • IRDB
    • CiNii Articles

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