Mars Spectrometry 2: Gas Chromatography -- Second place solution

TL;DR

This paper presents a second-place solution for NASA's Mars Spectrometry 2 challenge, employing 2D image representations of chromatography data and ensemble CNN models to achieve high accuracy.

Contribution

The paper introduces a novel approach using 2D image-like data representations and ensemble CNNs for gas chromatography data analysis in planetary science.

Findings

Achieved second place in the competition

Effective use of 2D image representations for chromatography data

Ensemble CNN models improved performance

Abstract

The Mars Spectrometry 2: Gas Chromatography challenge was sponsored by NASA and run on the DrivenData competition platform in 2022. This report describes the solution which achieved the second-best score on the competition's test dataset. The solution utilized two-dimensional, image-like representations of the competition's chromatography data samples. A number of different Convolutional Neural Network models were trained and ensembled for the final submission.