Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine
  Learning for Anomaly Detection
Roy T. Forestano Konstantin T. Matchev Katia Matcheva Eyup B. Unlu
The next generation of telescopes will yield a substantial increase in the
availability of high-resolution spectroscopic data for thousands of exoplanets.
The sheer volume of data and number of planets to be analyzed greatly motivate
the development of new, fast and efficient methods for flagging interesting
planets for reobservation and detailed analysis. We advocate the application of
machine learning (ML) techniques for anomaly (novelty) detection to exoplanet
transit spectra, with the goal of identifying planets with unusual chemical
composition and even searching for unknown biosignatures. We successfully
demonstrate the feasibility of two popular anomaly detection methods (Local
Outlier Factor and One Class Support Vector Machine) on a large public database
of synthetic spectra. We consider several test cases, each with different
levels of instrumental noise. In each case, we use ROC curves to quantify and
compare the performance of the two ML techniques.