Automatic Data Aggregation to Assist in the Systematic Classification of Small Lunar Craters

Author

Liam Powers, Ursinus CollegeFollow

Submission Date

7-21-2022

Document Type

Paper

Department

Physics & Astronomy

Faculty Mentor

Kassandra Martin-Wells

Comments

Presented during the 24th Annual Summer Fellows Symposium, July 22, 2022 at Ursinus College.

Project Description

Crater counting has been one of the dominant methods of characterizing surfaces of planetary bodies in the absence of material samples. Unfortunately, counts often rely on the subjective expertise of the counter, which limits the volume of reliable data that is accessible to researchers. Our work seeks to develop a quantifiable method of classifying individual craters within a count population to better determine a given crater’s age and origin. Recommendations are then generated in order to increase the accuracy of human counters, and improve the efficiency of the counting process. Preliminary work on the Moon uses LRO LOLA elevation data, Clementine UVVIS Optical Maturity data, Diviner Rock Abundance data, and Arecibo Green Bank Telescope ground-based 13cm circular radar polarization data to construct our recommendation model.

Recommended Citation

Powers, Liam, "Automatic Data Aggregation to Assist in the Systematic Classification of Small Lunar Craters" (2022). Physics and Astronomy Summer Fellows. 38.
https://digitalcommons.ursinus.edu/physics_astro_sum/38