Classification of Astronomical Objects Using Light Curve Profile

Theeranai Sangjan, Tossapon Boongoen, Natthakan Iam-On, James Mullaney

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

1 Citation (SciVal)

Abstract

Given the advancement in optical and imaging technology, new projects in astronomy commonly aim to produce a wide-field survey of astronomical objects. In particular, object-specific measurement of observed brightness overtime, so-called light curve, can be exploited to determine an object category, which signifies major properties and behavior. This is normally branded as a classification task, for which several models developed in machine learning community have been explored. Despite reported success in recent literature, imbalance data remains a significant factor that limits actual applications of those models. As such, this paper presents an empirical study based on a published data set of LSST light curve profiles, emphasizing the importance of data-level approach to solve imbalance data. Benchmarking classifiers such as k-nearest neighbor (KNN), decision tree and support vector machine (SVM) are employed in this investigation. Also, the use of PCA as a dimensionality reduction is employed to produce informative variables, which are likely to boost the classification performance.

Original languageEnglish
Title of host publication2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
EditorsTeen-Hang Meen
PublisherIEEE Press
Pages494-497
Number of pages4
ISBN (Electronic)9781728125015
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan
Duration: 03 Oct 201906 Oct 2019

Publication series

Name2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

Conference

Conference2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Country/TerritoryTaiwan
CityYunlin
Period03 Oct 201906 Oct 2019

Keywords

  • Astronomy
  • Classification and Imbalance data
  • Light curve
  • Time series

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