Classification of Astronomical Objects Using Light Curve Profile

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

doi:10.1109/ecice47484.2019.8942673

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, domain knowledge proves to be crucial here as characteristics of training set that is published to all competitors is largely dissimilar to that of the unseen test set. This is recently discussed by Sangjan et al [21], in which many classification methods perform well for only a few object classes. Yet, it might be possible to gain a robust alternative using an unsupervised approach that has been missing from the literature at large.…”

Section: Introductionmentioning

confidence: 97%

Profiling Astronomical Objects Using Unsupervised Learning Approach

Sangpetch¹,

Boongoen²,

Iam-On³

2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined types. As this challenge focuses on developing a predictive model that is robust to classifying unseen data, those previous attempts similarly encounter the lack of discriminate features, since distribution of training and actual test datasets are largely different. As a result, well-known classification algorithms prove to be sub-optimal, while more complicated feature extraction techniques may help to slightly boost the predictive performance. Given such a burden, this research is set to explore an unsupervised alternative to the difficult quest, where common classifiers fail to reach the 50% accuracy mark. A clustering technique is exploited to transform the space of training data, from which a more accurate classifier can be built. In addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning, a multiple-clustering alternative is also introduced with improved performance. In fact, it is able to yield a higher accuracy rate of 58.32% from 51.36% that is obtained using a simple clustering. For this difficult problem, it is rather good considering for those achieved by well-known models like support vector machine (SVM) with 51.80% and Naïve Bayes (NB) with only 2.92%.

show abstract

Section: Introductionmentioning

confidence: 97%