Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
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Abstract (arXiv)
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.
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References · 4
- 2017Learning how to Active Learn: A Deep Reinforcement Learning Approachcited by 79
- 2019Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Datacited by 15
- 2021Tails: Chasing Comets with the Zwicky Transient Facility and Deep Learningcited by 6
- 2021A Two-Stage Deep Learning Detection Classifier for the ATLAS Asteroid Surveycited by 5
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