Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 488, Issue 4, Pages 4858-4872Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stz1999
Keywords
methods: data analysis; methods: statistical; stars: variables: general
Categories
Funding
- UK Newton Fund as part of the Development in Africa with Radio Astronomy (DARA) Big Data project
- European Research Council (ERC) under the European Union [694745]
- Imperial President's PhD Scholarship
- STFC [ST/R001898/1, ST/T001399/1, ST/P000649/1] Funding Source: UKRI
Ask authors/readers for more resources
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data describing 11 types of variable stars from the Catalina Real-Time Transient Survey (CRTS), we illustrate how to capture the most important information from computed features and describe detailed methods of how to robustly use information theory for feature selection and evaluation. We apply three machine learning algorithms and demonstrate how to optimize these classifiers via cross-validation techniques. For the CRTS data set, we find that the random forest classifier performs best in terms of balanced accuracy and geometric means. We demonstrate substantially improved classification results by converting the multiclass problem into a binary classification task, achieving a balanced-accuracy rate of similar to 99percent for the classification of delta Scuti and anomalous Cepheids. Additionally, we describe how classification performance can be improved via converting a flat multiclass' problem into a hierarchical taxonomy. We develop a new hierarchical structure and propose a new set of classification features, enabling the accurate identification of subtypes of Cepheids, RRLyrae, and eclipsing binary stars in CRTS data.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available