Submission Date

7-19-2024

Document Type

Paper

Department

Physics & Astronomy

Faculty Mentor

Kassandra Martin-Wells

Student Contributor

McKenzie Snyder

Second Student Contributor

Gavin Soueidan

Comments

Presented during the 26th Annual Summer Fellows Symposium, July 19, 2024 at Ursinus College.

Project Description

Crater identification is important for investigating and determining the age of planetary surfaces. Based on the number of primary craters—which are formed from a bolide impacting a planetary surface—one can determine the surface’s absolute model age, but the secondary craters—which are formed from the many fragments ejected during a primary crater’s creation—contaminate that count. The abundant variability in crater identification methods is a prominent issue in the field of planetary science. In addition, there is no single characteristic that can set these craters apart due to the processes involved in their formation and degradation. This is why we aim to develop a semi-automated data processing procedure that will compile measurements from multiple datasets that have been shown to highlight differences between lunar primary and secondary craters. This will aid human investigators in making crater classifications, which will be stored, providing a record that will enable future researchers to make more reproducible results. We will utilize photographic and radar data, which can be visualized, extracted, and referenced using publicly available remote sensing data of the lunar surface, accessible through the Java Mission-planning and Analysis for Remote Sensing (JMARS) geospatial information system software. Our current focus is to write procedures in the Interactive Data Language that could altogether be used to classify craters. This work includes a literature review of some of the techniques that previous workers have used to differentiate secondary craters from primary craters. I utilized this knowledge to create a procedure that returns crater classification decisions based on spatial clustering parameters.

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