Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning

In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field all-sky surveys.

We will begin the chapter by discussing the challenges of identifying known and unknown active, extended Solar System objects in the presence of stellar-type sources and the application of classical pre-ML identification techniques and their limitations.

We will then transition to the discussion of implementing ML techniques to address the challenge of extended object identification.

We will finish with prospective future methods and the application to future surveys such as the Vera C. Rubin Observatory.

Bryce T. Bolin, Michael W. Coughlin

Comments: 25 pages, 9 figures, accepted chapter in Machine Learning for Small Bodies in the Solar System, Valerio Carruba, Evgeny Smirnov, and Dagmara Oszkiewicz, Elsevier, 2024, p. 209-227
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2409.15261 [astro-ph.EP] (or arXiv:2409.15261v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2409.15261
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Submission history
From: Bryce Bolin
[v1] Mon, 23 Sep 2024 17:56:32 UTC (3,285 KB)
https://arxiv.org/abs/2409.15261
Astrobiology