In this work we present an automated software package to assist with the detection and identification of emission lines captured from the Atacama Large Millimeter/submillimeter Array (ALMA). ALMA collects spectral data from planetary atmospheres. The molecular composition of an atmosphere can be measured by analyzing the absorption and emission at various wavelengths. This process is typically manual and time consuming. We present a framework for automating the analysis based on identifying candidate lines and matching the emission pattern to the JPL spectral line atlas. We proceeded to evaluate our algorithm against interferometric observations of Titan collected for ALMA project 2011.0.00319.S. Our algorithm re-confirmed the molecular species identified in previously published work and proposed additional species for consideration. Additionally, we will analyze other spectra of Titan to further validate our technique. This approach is semi-automatic and intended to computationally assist rather than replace the human labeling process. Figure 1 demonstrates example output from our tool. Users can quickly identify spikes and associated candidate molecular species in a spectrum, guiding their analysis.

Automated spectroscopic detection of molecular species in planetary atmospheres using machine learning

ACS Spring Meeting 2021

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