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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References
Aad, G., et al.: The ATLAS Simulation Infrastructure. Eur. Phys. J. C70, 823–874 (2010). DOI https://doi.org/10.1140/epjc/s10052-010-1429-9
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). URL https://www.tensorflow.org/. Software available from tensorflow.org
Adam-Bourdarios, C., Cowan, G., Germain, C., Guyon, I., Kégl, B., Rousseau, D.: The Higgs boson machine learning challenge. In: HEPML@ NIPS, pp. 19–55 (2014). URL http://www.jmlr.org/proceedings/papers/v42/cowa14.pdf
Amrouche, S., et al.: The Tracking Machine Learning Challenge: Throughput phase. in preparation
Amrouche, S., et al.: Track reconstruction at LHC as a collaborative data challenge use case with RAMP. EPJ Web Conf. 150, 00015 (2017). DOI https://doi.org/10.1051/epjconf/201715000015
Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Pacific Symposium on Biocomputing, pp. pp. 6–17 (2002)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975). DOI https://doi.org/10.1145/361002.361007. URL http://doi.acm.org/10.1145/361002.361007
Flavours of Physics: Finding τ → μμμ. URL https://www.kaggle.com/c/flavours-of-physics
the Large Hadron Collider at CERN. URL https://home.cern/topics/large-hadron-collider
TrackML data set to appear on . URL http://opendata.cern.ch
TrackML data set to appear on . URL https://archive.ics.uci.edu/ml/datasets.html
TrackML particle tracking challenge main site. URL https://sites.google.com/site/trackmlparticle/
TrackML Particle Tracking Challenge on Codalab. URL https://competitions.codalab.org/competitions/20112
TrackML Particle Tracking Challenge on Kaggle. URL https://www.kaggle.com/c/trackml-particle-identification
Chollet, F., et al.: Keras. https://keras.io (2015)
CMS Collaboration: The Phase-2 Upgrade of the CMS Tracker. Tech. Rep. CMS-TDR-014, CERN, Geneva (2017)
Collaboration, A.: Technical Design Report for the ATLAS Inner Tracker Pixel Detector. Tech. Rep. ATLAS-TDR-030, CERN, Geneva (2017)
Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Tech. rep., Artificial Intelligence Center (1971)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, pp. 226–231. AAAI Press (1996)
Farrell, S., et al.: The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking. EPJ Web Conf. 150, 00003 (2017). DOI https://doi.org/10.1051/epjconf/201715000003
Ferreira, D.R.: Code for TrackML Accuracy challenge. URL https://github.com/diogoff/trackml-100
Finnie, L., Finnie, N.: Code for TrackML Accuracy challenge. URL https://github.com/jliamfinnie/kaggle-trackml
Gorbunov, S.: Code for TrackML Accuracy challenge. URL https://github.com/sgorbuno/TrackML_CombinatorialTracker
Gumpert, C., Salzburger, A., Kiehn, M., Hrdinka, J., Calace, N.: ACTS: from ATLAS software towards a common track reconstruction software. J. Phys. Conf. Ser. 898(4), 042011 (2017). DOI https://doi.org/10.1088/1742-6596/898/4/042011
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). DOI https://doi.org/10.1162/neco.1997.9.8.1735. URL http://dx.doi.org/10.1162/neco.1997.9.8.1735
Meilă, M.: Comparing clusterings—an information based distance. J. Multivar. Anal. 98(5), 873–895 (2007)
Pérez, F., Granger, B.E.: IPython: a system for interactive scientific computing. Computing in Science and Engineering 9(3), 21–29 (2007). DOI https://doi.org/10.1109/MCSE.2007.53. URL https://ipython.org
Puget, J.F.: Code for TrackML Accuracy challenge. URL https://github.com/jfpuget/Kaggle_TrackML
Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)
Reina, Y., Xylouris, T.: Code for TrackML Accuracy challenge. URL https://github.com/tx1985/kaggle-trackML
Sabes, D.: L1 track triggering with associative memory for the CMS HL-LHC tracker. JINST 9(11), C11014 (2014). URL https://cds.cern.ch/record/2025864
Sjöstrand, T., Mrenna, S., Skands, P.: A brief introduction to PYTHIA 8.1. Computer Physics Communications 178(11), 852–867 (2008). DOI https://doi.org/10.1016/j.cpc.2008.01.036
TrackML team: TrackML helper library . URL https://github.com/LAL/trackml-library
Wilcoxon, F.: Individual Comparisons by Ranking Methods. Biometrics Bulletin 1(6), 80–83 (1945). DOI https://doi.org/10.2307/3001968. URL http://dx.doi.org/10.2307/3001968
Wind, J.S.: Code for TrackML Accuracy challenge. URL https://github.com/top-quarks/top-quarks
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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