Light curve classification with DistClassiPy: A new distance-based classifier

Creators: Chaini, S.; Mahabal, A.¹; Kembhavi, A.; Bianco, F. B.

1. California Institute of Technology

Abstract

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. Tree-based (e.g. Random Forests) and deep learning models represent the current standard in the field. We explore the use of different distance metrics to aid in the classification of objects. For this, we developed a new distance metric based classifier called DistClassiPy. The direct use of distance metrics is an approach that has not been explored in time-domain astronomy, but distance-based methods can aid in increasing the interpretability of the classification result and decrease the computational costs. In particular, we classify light curves of variable stars by comparing the distances between objects of different classes. Using 18 distance metrics applied to a catalog of 6,000 variable stars in 10 classes, we demonstrate classification and dimensionality reduction. We show that this classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. We have made DistClassiPy open-source and accessible at https://pypi.org/project/distclassipy/ with the goal of broadening its applications to other classification scenarios within and beyond astronomy.

Copyright and License

Acknowledgement

SC would like to acknowledge the support received from Prof. Sukanta Panda and the Department of Physics, IISER Bhopal. This work originated as part of SC’s Master’s thesis at IISER Bhopal.

Based on observations obtained with the Samuel Oschin Telescope 48-inch and the 60-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under Grants No. AST-1440341 and AST-2034437 and a collaboration including current partners Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, Deutsches Elektronen-Synchrotron and Humboldt University, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, Trinity College Dublin, Lawrence Livermore National Laboratories, IN2P3, University of Warwick, Ruhr University Bochum, Northwestern University and former partners the University of Washington, Los Alamos National Laboratories, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW.

The authors acknowledge the support of the Vera C. Rubin Legacy Survey of Space and Time Science Collaborations¹⁵ and particularly of the Transient and Variable Star Science Collaboration ¹⁶ (TVS SC) that provided opportunities for collaboration and exchange of ideas and knowledge. The authors wish to thank the anonymous referee for insightful comments that lead us to improve our manuscript. This research has made use of NASA’s Astrophysics Data System Bibliographic Services.

Contributions

S. Chaini: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. A. Mahabal: Writing – review & editing, Visualization, Supervision, Resources, Methodology, Investigation, Formal analysis, Conceptualization. A. Kembhavi: Writing – review & editing, Supervision, Resources. F.B. Bianco: Writing – review & editing, Visualization, Supervision, Resources, Methodology, Investigation, Funding acquisition, Conceptualization.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional Information

During the preparation of this work the author(s) used ChatGPT to generate LaTeX code templates. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

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