Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars

TL;DR

This paper presents a machine learning approach to detect fresh impact craters on Mars, reducing observational bias towards low thermal inertia areas and discovering new impacts confirmed by high-resolution images.

Contribution

It introduces a trained machine learning classifier that increases detection of impacts in high thermal inertia areas, addressing bias in current impact inventories.

Findings

Discovered 69 new impacts confirmed by HiRISE images.

Machine learning helps reduce bias towards low thermal inertia regions.

Partitioning candidates by thermal inertia improves impact detection.

Abstract

The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark scours and rays that make them easier to detect. It is expected that impacts occur at a similar rate in areas of higher thermal inertia, but those impacts are under-detected. This study investigates the use of a trained machine learning classifier to increase the detection of fresh impacts on Mars using CTX data. This approach discovered 69 new fresh impacts that have been confirmed with follow-up HiRISE images. We found that examining candidates partitioned by thermal inertia (TI) values, which is only possible due to the large number of machine learning candidates, helps reduce the observational bias and increase the number of known high-TI impacts.