Astronomy16 papers
Artificial Intelligence and Machine Learning in Planetary Science
A companion collection for the ACM 2026 talk by Valerio Carruba
VC
Valerio Carruba
All Reviews
AstronomyWorth Reading
intermediate
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
Bolin, Bryce & Coughlin, M. W. (2024)
- Published
- Sep 23, 2024
- Journal
- arXiv (Cornell University)
- DOI
- 10.48550/arXiv.2409.15261
At a GlanceAI
The chapter outlines how machine learning can improve detection and localization of cometary activity in wide-field sky surveys.
SummaryAI
This chapter is important because finding active comets and other extended Solar System objects is difficult in survey data crowded with star-like sources. It reviews the limits of older, pre-machine-learning identification methods and explains how machine learning can better identify and localize cometary activity in both ground- and space-based surveys. The chapter also looks ahead to future survey applications, including the Vera C. Rubin Observatory, highlighting the growing role of automated analysis in Solar System discovery.
Method SnapshotAI
The chapter uses a comparative review of classical detection methods and machine-learning approaches for survey-based identification of cometary activity.
BackgroundAI
Familiarity with astronomy survey data, Solar System objects, and basic machine learning concepts is helpful.
This chapter provides an in-depth review of machine-learning methods for identifying cometary activity in wide-field astronomical surveys. By reviewing both classical approaches and modern AI techniques, it offers an accessible introduction to the challenges of detecting active solar system objects and discusses future applications to next-generation surveys such as the Vera C. Rubin Observatory. A valuable starting point for researchers entering this rapidly developing field.
— VC