Albertsson, K;
Altoe, P;
Anderson, D;
Andrews, M;
Araque Espinosa, JP;
Aurisano, A;
Basara, L;
... Stockdale, I; + view all
(2018)
Machine Learning in High Energy Physics Community White Paper.
In:
Journal of Physics: Conference Series.
(pp. 022008).
IOP: Seattle, WA, USA.
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Abstract
Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
| Type: | Proceedings paper |
|---|---|
| Title: | Machine Learning in High Energy Physics Community White Paper |
| Event: | 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2017) |
| Location: | Seattle, WA, USA |
| Dates: | 21- 25 August 2017 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1742-6596/1085/2/022008 |
| Publisher version: | https://doi.org/10.1088/1742-6596/1085/2/022008 |
| Language: | English |
| Additional information: | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://creativecommons.org/licenses/by/3.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10073795 |
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