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

TL;DR

This paper discusses how machine learning techniques can improve the identification and localization of cometary activity in Solar System objects within wide-field surveys, addressing limitations of classical methods and exploring future applications.

Contribution

It introduces ML-based approaches for detecting active Solar System objects, enhancing identification accuracy over traditional techniques, and discusses future survey applications like the Vera C. Rubin Observatory.

Findings

ML methods improve detection of cometary activity

Classical techniques have limitations in crowded fields

Future surveys will benefit from ML integration

Abstract

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.