Using machine learning to reduce observational biases when detecting new impacts on Mars.

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. • Machine learning classifiers can help increase the detection of rare fresh impacts on Mars. • The most-confident machine learning candidates preserve observational biases in the manually identified training examples. • Analyzing candidates partitioned by thermal inertia bins reduces observational bias. [ABSTRACT FROM AUTHOR]