Arxiv:2010.15996V1 [Astro-Ph.IM] 29 Oct 2020 Keywords: ARIEL, Exoplanets, Transit Photometry, Light Curves, Stellar Spots, Machine Learning
Draft version November 2, 2020 Typeset using LATEX default style in AASTeX63 Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots Nikolaos Nikolaou ,1 Ingo P. Waldmann ,1 Angelos Tsiaras ,1 Mario Morvan ,1 Billy Edwards ,1 Kai Hou Yip ,1 Giovanna Tinetti ,1 Subhajit Sarkar,2 James M. Dawson,2 Vadim Borisov,3 Gjergji Kasneci,3 Matej Petkovic,´ 4 Tomaˇz Stepiˇsnik,4 Tarek Al-Ubaidi,5, 6 Rachel Louise Bailey ,6 Michael Granitzer ,7 Sahib Julka ,7 Roman Kern,8 Patrick Ofner ,8 Stefan Wagner,9 Lukas Heppe ,10 Mirko Bunse ,10 and Katharina Morik 10 1Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, UK 2School of Physics and Astronomy, Cardiff University, The Parade, Cardiff, CF24 3AA, UK 3Department of Computer Science, University of Tuebingen, Tuebingen, Germany 4Joˇzef Stefan Institute, Ljubljana, Slovenia 5DCCS, Austria 6Space Research Institute, Austrian Academy of Sciences, Austria 7Chair of Data Science, University of Passau, Germany 8Know-Center GmbH - Research Center for Data-Driven Business & Big Data Analytics, Austria 9Commission for Astronomy, Austrian Academy of Sciences, Graz, Austria 10Artificial Intelligence Group, TU Dortmund University, Germany Submitted to ApJ ABSTRACT The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data.
[Show full text]