Machine learning: how to get more out of HEP data and the Higgs Boson Machine Learning Challenge

Marcin Wolter

doi:10.1117/12.2205254

11 September 2015 Machine learning: how to get more out of HEP data and the Higgs Boson Machine Learning Challenge

Marcin Wolter

Proceedings Volume 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015; 96622I (2015) https://doi.org/10.1117/12.2205254
Event: XXXVI Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (Wilga 2015), 2015, Wilga, Poland

Abstract

Multivariate techniques using machine learning algorithms have become an integral part in many High Energy Physics (HEP) data analyses. The article shows the gain in physics reach of the physics experiments due to the adaptation of machine learning techniques. Rapid development in the field of machine learning in the last years is a challenge for the HEP community. The open competition for machine learning experts “Higgs Boson Machine Learning Challenge” shows, that the modern techniques developed outside HEP can significantly improve the analysis of data from HEP experiments and improve the sensitivity of searches for new particles and processes.

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Marcin Wolter "Machine learning: how to get more out of HEP data and the Higgs Boson Machine Learning Challenge", Proc. SPIE 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015, 96622I (11 September 2015); https://doi.org/10.1117/12.2205254

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