The Higgs Machine Learning Challenge. Issue 7 (December 2015)

• Record Type: Journal Article
• Authors: Adam-Bourdarios, C; Cowan, G; Germain-Renaud, C; Guyon, I; Kégl, B; Rousseau, D
• Is Part Of: Journal of physics. Volume 664:Issue 7(2015)
• Publication Date: 2015-12
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/664/7/072015
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying 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 to promote communication of new ideas between the Machine Learning (ML) and HEP communities. In this regard it was a resounding success, with almost 2, 000 participants from HEP, ML and other areas. The process of understanding and integrating the new ideas, particularly from ML into HEP, is currently underway.