The Higgs Machine Learning Challenge

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Adam-Bourdarios, C.^1 ; Cowan, G.^2 ; Germain-Renaud, C.^3 ; Guyon, I.^4 ; Kégl, B.^1 ; Rousseau, D.^1

Laboratoire de l'Accélérateur Linéaire, Orsay, France^1

Department of Physics, Royal Holloway, University of London, United Kingdom^2

Laboratoire de Recherche en Informatique, Orsay, France^3

ChaLearn

CA, United States^4

关键词: ATLAS experiment;    Higgs boson;    Kinematic variables;    Search region;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/7/072015/pdf
DOI  :  10.1088/1742-6596/664/7/072015

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.