Machine-assisted classification of potential biosignatures in earth-like exoplanets using low signal-to-noise ratio transmission spectra

Abstract: The search for atmospheric biosignatures in Earth-like exoplanets is one of the most pressing challenges in observational astrobiology. Detecting biogenic gases in terrestrial planets requires high-resolution observations and long integration times. In this work, we developed and tested a general machine-learning methodology designed to classify transmission spectra with low Signal-to-Noise Ratio (SNR) according to their potential to contain biosignatures or bioindicators. To achieve this, we trained a set of models capable of classifying noisy transmission spectra (including stellar contamination) as containing methane, ozone, and/or water (multilabel classification), or simply as being interesting for follow-up observations (binary classification). The models were trained using $\sim10^7$ synthetic spectra of planets similar to TRAPPIST-1 e, generated with the package MultiREx. The trained algorithms correctly classified most of the test planets with transmission spectra having an SNR as low as 4, containing methane and/or ozone at mixing ratios similar to those of modern and Proterozoic Earth. Tests on realistic synthetic spectra, based on the current Earth's atmosphere, indicate that some of our models would classify most inhabited terrestrial planets observed with JWST/NIRSpec PRISM around M-dwarfs at distances similar to or smaller than that of TRAPPIST-1 e as likely to contain bioindicators, using 4 to 10 transits. These results have significant implications for the design of observing programs and future campaigns. Machine-assisted strategies, such as the one presented here, could greatly optimize the use of JWST resources for biosignature and bioindicator searches, while maximizing the chances of a real discovery through dedicated follow-up observations of promising candidates.