Share Email Print
cover

Proceedings Paper

Machine learning: how to get more out of HEP data and the Higgs Boson Machine Learning Challenge
Author(s): Marcin Wolter
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 11 September 2015
PDF: 9 pages
Proc. SPIE 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015, 96622I (11 September 2015); doi: 10.1117/12.2205254
Show Author Affiliations
Marcin Wolter, Institute of Nuclear Physics (Poland)


Published in SPIE Proceedings Vol. 9662:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015
Ryszard S. Romaniuk, Editor(s)

© SPIE. Terms of Use
Back to Top