EPSC Abstracts
Vol. 17, EPSC2024-136, 2024, updated on 03 Jul 2024
https://doi.org/10.5194/epsc2024-136
Europlanet Science Congress 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Identification and Localization of Cometary Activityin Solar System Objects with Machine Learning

Bryce Bolin
Bryce Bolin
  • University of Washington, DIRAC, Astronomy, Seattle, United States of America (bolin.astro@gmail.com)

In this session, 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.

How to cite: Bolin, B.: Identification and Localization of Cometary Activityin Solar System Objects with Machine Learning, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-136, https://doi.org/10.5194/epsc2024-136, 2024.