Abstract
This paper reports the results of an experiment in high energy physics: using the power of the “crowd” to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100,000 points, the participants had to connect them into about 10,000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document.
Participants, others are organizers: Jean-François Puget, Trian Xylouris, Sergey Gorbunov, Nicole Finnie, Liam Finnie, Diogo R. Ferreira, Johan Sokrates Wind, Yuval Reina.
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Acknowledgements
The team would like to thank CERN for allowing the use of the dataset, and Kaggle for hosting it. We are very grateful to our generous sponsors without which the challenges would not have been possible. Platinum sponsors: Kaggle, Nvidia and Université de Genève. Gold sponsors: Chalearn, ERC mPP and DataIA. Silver sponsors: CERN Openlab, Paris-Saclay CDS, INRIA, ERC RECEPT, Common Ground, Université Paris Sud, INQNET, Fermilab and pyTorch. TG acknowledges the support of the Swiss National Science Foundation under the grant 200020_181984. SG acknowledges the support of the German BMBF ministry. This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 724777 “RECEPT”, No 772369 “mPP” and No 654168 “AIDA-2020”. In addition, the organizers would like to thank participant Pei-Lien Chou “outrunner” for major contributions, Maggie Demkin and Walter Reade at Kaggle and the members of the International Advisory Committee: Markus Elsing (CERN), Frank Gaede (DESY), Alison Lowndes (Nvidia), Maurizio Pierini (CERN), Danilo Rezende (Google DeepMind), Marc Schoenauer (INRIA-Saclay) and Svyatoslav Voloshynovskyy (U Genève).
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Amrouche, S. et al. (2020). The Tracking Machine Learning Challenge: Accuracy Phase. In: Escalera, S., Herbrich, R. (eds) The NeurIPS '18 Competition. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-29135-8_9
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