Detecting unusual chemical signatures using autoencoder-based anomaly detection

The study of exoplanetary atmospheres is influenced by models and assumptions based on Earth-like chemistry, because it is what we know best. This perspective can introduce biases in how we interpret spectroscopic data. With the upcoming Ariel mission, we will soon have access to a large, uniform dataset of thousands of exoplanet atmospheres. This presents both an opportunity and a challenge: how can we quickly identify atmospheres that do not follow expected chemical patterns?

The main goal of this study is to test the use of machine learning, specifically autoencoder-based anomaly detection, to identify exoplanet atmospheres with unexpected chemical signatures. This could help detect interesting targets early in large-scale surveys and avoid missing important atmospheric diversity.

We use the publicly available Atmospheric Big Challenge (ABC) database (Changeat & Yip 2023), which contains 105,887 simulated exoplanet transmission spectra generated with a wide range of planetary parameters. All these spectra are based on a ”free chemistry” approach, where molecular abundances are constant throughout the atmosphere, with the abundance value allowed to change unconstrained by equilibrium chemistry. We then use the set-up introduced in Forestano et al. (2023), where the dataset is divided into normal and anomalous samples. An anomalous atmosphere is considered to be one which includes an additional absorber that the training set does not have. Planets with normal spectra are those where this absorber (for example, CO or CO2) is essentially absent.

We train an autoencoder on the normal sample of the ABC database. We look for anomalies in the input space, the latent space as well as the reconstructed space. Anomaly detection in the latent space gives the clearest separation between normal and anomalous spectra. We test the method across different signal-to-noise ratios. This is specifically interesting for molecules that show weak spectral signatures or in the case of spectra with higher level of noise.

Autoencoder-based anomaly detection is a useful tool for identifying exoplanet atmospheres that differ from expected patterns. This approach does not require labeling or predefined thresholds and can process large datasets automatically. It could help us prioritize unusual targets in missions like Ariel, where time for detailed follow-up is limited. In future work, we plan to include equilibrium chemistry in the anomaly set, which will allow us to detect even more diverse chemical behaviors in exoplanet atmospheres.