On the classification and feature relevance of multiband light curves


Edeş Huyal F. K., Çataltepe Z., Kahya E. O.

Astronomical Journal, cilt.161, sa.4, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 161 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3847/1538-3881/abdecf
  • Dergi Adı: Astronomical Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, INSPEC, DIALNET
  • Anahtar Kelimeler: Astronomy data analysis, Light curve classification, Wavelet analysis
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2021. The American Astronomical Society. All rights reserved.With an expected torrent of data from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the need for automated identification of noisy and sparse light curves will increase drastically. In this paper, we performed classification of multiband astronomical light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) data set via boosted neural nets, boosted decision trees, and a voted classifier for 14 astronomical categories. In order to deal with noisy features, we used wavelet decomposition together with feature selection. We also performed a feature ranking method using a neural network. Our method may be considered an alternative to random forests, which is known to favor features with more categories as relevant. We also investigated the class importance with neural nets using a one-versus-all approach which reduces the multiclass problem to a binary class problem.