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<div style="font-size:12px; font-family:HelveticaNeue">[Apologies for multiple postings] </div>
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<div style="font-size:12px; font-family:HelveticaNeue">Call for Participation</div>
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<div style="font-size:12px; font-family:HelveticaNeue">ECML-PKDD 2019 Discovery Challenge - Correcting Transiting Exoplanet Light Curves for Stellar Spots (Ariel Machine Learning & Stellar Activity Challenge)</div>
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<div style="font-size:12px; font-family:HelveticaNeue">Organised and Sponsored by the European Space Agency (ESA) M4 ARIEL Mission Consortium and the University College London</div>
<div style="font-size:12px; font-family:HelveticaNeue">Hosted by the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2019</div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Motivation & Goals*** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">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 and data
mining methodology. For instance, the most successful method for detecting exoplanets, transit photometry –measuring the faint decrease in incoming stellar light as an exoplanet passes between the Earth and a target star– is very sensitive to the presence
of stellar spots, i.e. areas of the star that are colder and emit fewer light. The current approach is to identify the effects of such spots visually and correct for them manually or discard the data. As a first step to automate this process, we propose a
regular competition on data generated by a simulator of the European Space Agency’s upcoming Ariel mission, whose objective is to characterise the atmosphere of 1000 exoplanets. The data consist of light curves (i.e. time series of the light received by the
observation instrument from the target star-planet system) corrupted by stellar spots, along with auxiliary observation information. The goal is to correct the light curves for the presence of stellar spots, by predicting the relative planet-to-star radius
ratio. This is a yet unsolved problem in the community. Solving it will mean improving our understanding of the characteristics of currently confirmed exoplanets, potentially recognising false positive / false negative detections and improving our ability
to analyse new observations – primarily but not limited to those expected from Ariel– without the need to equip new telescopes with additional instruments with all the extra costs this implies.</div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Dataset & Task Details*** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">Task: Supervised learning, multi-target regression; all variables are continuous.</div>
<div style="font-size:12px; font-family:HelveticaNeue">Features: Each training datapoint consists of a set of 55 noisy light curves (one per wavelength, each being a timeseries of 300 timesteps) and a set of 6 additional stellar and planetary parameters. </div>
<div style="font-size:12px; font-family:HelveticaNeue">Targets: The goal is to predict a set of 55 values (relative radii, one per wavelength) for any datapoint.</div>
<div style="font-size:12px; font-family:HelveticaNeue">Dataset size: The size of the dataset is ~20Gb.</div>
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<div style="font-size:12px; font-family:HelveticaNeue">For a more detailed description, please visit the competition website at:</div>
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<div style="font-size:12px; font-family:HelveticaNeue"><a href="https://ariel-datachallenge.azurewebsites.net/ML">https://ariel-datachallenge.azurewebsites.net/ML</a></div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Participation *** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">Participants of this challenge will submit the predictions of their models on the provided test dataset. The ground truth will be released to the participants after the end of the competition.</div>
<div style="font-size:12px; font-family:HelveticaNeue">The solutions will be automatically ranked and the participants will obtain immediate feedback in the leaderboard maintained in the site. The 2 top-ranked participants will be invited to provide a brief
description of their solution (describing data preprocessing steps, models and algorithms used) in the week after the competition closes. The 2 top-ranked participants will then be eligible to prizes, provided they beat the baseline, and no plagiarism or test
set leakage has occured. </div>
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<div style="font-size:12px; font-family:HelveticaNeue">To participate, please visit the competition website at: </div>
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<div style="font-size:12px; font-family:HelveticaNeue"><a href="https://ariel-datachallenge.azurewebsites.net/ML">https://ariel-datachallenge.azurewebsites.net/ML</a></div>
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<div style="font-size:12px; font-family:HelveticaNeue">and follow the instructions to learn details about the problem, data, submission format, evaluation protocol and the baseline solution.</div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Important dates *** (All times are in AoE time) </div>
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<div style="font-size:12px; font-family:HelveticaNeue">* April 15th, 2019: Beginning of the competition, release of training dataset and test dataset (w/o ground truth). Participants can start submitting model predictions on test set and obtaining immediate
feedback in the leaderboard. </div>
<div style="font-size:12px; font-family:HelveticaNeue">* Aug 15th, 2019: Competition closes, release of ground truth. Top-ranked participants are invited to submit a brief description of their solution.</div>
<div style="font-size:12px; font-family:HelveticaNeue">* Aug 22nd, 2019: Deadline for submitting description of solution. Organisers start checking solutions for plagiarism and test set leakage. </div>
<div style="font-size:12px; font-family:HelveticaNeue">* Aug 25th, 2019: Announcement of the competition winners. (Tentative, subject to successful checks and collaboration from participants) </div>
<div style="font-size:12px; font-family:HelveticaNeue">* Sept 16th - 20th, 2019: ECML-PKDD 2019. </div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Prizes *** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">The 2 top-ranked participants (provided they beat the baseline) will be awarded with a free registration to the ECML-PKDD 2019, to be held in Würzburg, Germany, from September 16 - 20, 2019. </div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Dissemination of results *** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">The 5 top-ranked participants (provided they beat the baseline) will be invited to present their solutions at ECML-PKDD 2019, to be held in Würzburg, Germany, from September 16 - 20, 2019. The authors of
solutions that are of interest to the organisers will be invited to participate to larger scale collaborations in the context of the Ariel mission and beyond.</div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Organizing team *** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">Nikolaos Nikolaou - UCL, England - <a href="mailto:n.nikolaou@ucl.ac.uk">n.nikolaou@ucl.ac.uk</a> - (Main organizer) </div>
<div style="font-size:12px; font-family:HelveticaNeue">Ingo P. Waldmann - UCL, England</div>
<div style="font-size:12px; font-family:HelveticaNeue">Subhajit Sarkar - University of Cardiff, Wales</div>
<div style="font-size:12px; font-family:HelveticaNeue">Angelos Tsiaras - UCL, England</div>
<div style="font-size:12px; font-family:HelveticaNeue">Billy Edwards - UCL, England</div>
<div style="font-size:12px; font-family:HelveticaNeue">Mario Morvan - UCL, England</div>
<div style="font-size:12px; font-family:HelveticaNeue">Kai Hou Yip - UCL, England</div>
<div style="font-size:12px; font-family:HelveticaNeue">Giovanna Tinetti - UCL, England</div>
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<div style="font-size:12px; font-family:HelveticaNeue">*** Contact Email *** </div>
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<div style="font-size:12px; font-family:HelveticaNeue">For technical issues or questions, please contact us at:</div>
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<div style="font-size:12px; font-family:HelveticaNeue"><a href="mailto:exoai.ucl@gmail.com">exoai.ucl@gmail.com</a></div>
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<div style="font-family:HelveticaNeue; font-size:12px">Nikolaos Nikolaou</div>
<div style="font-family:HelveticaNeue; font-size:12px">Postdoctoral Researcher</div>
<div style="font-family:HelveticaNeue; font-size:12px">Department of Physics & Astronomy, UCL</div>
<div style="font-family:HelveticaNeue; font-size:12px"><a href="https://nnikolaou.github.io/" class="OWAAutoLink" title="https://nnikolaou.github.io/
Cmd+Click or tap to follow the link">https://nnikolaou.github.io</a><br>
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