Correcting Transiting Exoplanet Light Curves for Stellar Spots: A Machine Learning Challenge for the ESA Ariel Space Mission
Abstract
The field of exoplanet discovery and characterisation has been growing rapidly in the last decade. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most successful method for detecting exoplanets, transit photometry, is very sensitive to the presence of stellar spots. The current approach is to identify the effects of spots visually and correct for them manually or discard the data. As a first step to automate this process, we are organising a competition for the 2019 European Conference of Machine Learning (ECML) on data generated by ArielSim, the simulator of the European Space Agency's upcoming Ariel mission, whose objective is to characterise the atmosphere of 1000 exoplanets. The data consists of pairs of light curves corrupted by stellar spots and the corresponding clean ones, along with auxiliary observation information. The goal is to correct light curves for the presence of stellar spots (multiple signal denoising). This is a yet unsolved problem in the community. In this talk we will discuss the problem, the impact of a solution, introduce the basics of machine learning and present the outline of the competition as well as initial baseline solutions.
- Publication:
-
AAS/Division for Extreme Solar Systems Abstracts
- Pub Date:
- August 2019
- Bibcode:
- 2019ESS.....433007N