Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
This work addresses a domain-specific problem in astronomy for researchers analyzing survey data, but it appears incremental as it reviews existing approaches rather than presenting new results.
The paper tackles the problem of identifying and localizing cometary activity in Solar System objects from wide-field all-sky surveys, addressing challenges like distinguishing extended objects from stellar sources. It discusses the transition from classical techniques to machine learning methods and their application to future surveys like the Vera C. Rubin Observatory.
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.