The Higgs Machine Learning Challenge

C Adam-Bourdarios^{1}, \underline{G Cowan}^{2}, C Germain-Renaud^{3}, I Guyon^{4}, B Kégl^{1}, D Rousseau^{1}

1 Laboratoire de l’Accélérateur Linéaire, Orsay, France 2 Physics Department, Royal Holloway, University of London, UK 3 Laboratoire de Recherche en Informatique, Orsay, France 4 ChaLearn, California, USA

E-mail: g.cowan@rhul.acuk

Abstract. The Higgs Machine Learning Challenge was an open data analysis competition that took place between June and September 2014. Samples of simulated data from the ATLAS Experiment corresponding to signal events with Higgs boson decays to τ^{+}τ^{−} together with background events were made available to the public through the website of the data-science organization Kaggle (kaggle.com). Participants attempted to identify the search region in a space of 30 kinematic variables that would maximize the expected discovery significance of the signal process. One of the primary goals of the Challenge was promote communication of new ideas between the Machine Learning (ML) and HEP communities. In this regard it was a resounding success, with almost 2000 participants from HEP, ML and other areas. The process of understanding and integrating the new ideas, paraticularly from ML into HEP, is currently underway.

1. Introduction Here is the introduction.

It will include a reference to [1]

References

[1] Cowan G, Cranmer K, Gross E and Vitells O 2011 Eur. Phys. J. C 71 1554 (Preprint arXiv:1007.1727)