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Machine Learning in High Energy Physics Community White Paper
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
University of Florida.
OProject and University of Antioquia.
Antal upphovsmän: 1192018 (Engelska)Ingår i: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 1085, artikel-id 022008Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Institute of Physics (IOP), 2018. Vol. 1085, artikel-id 022008
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URN: urn:nbn:se:ltu:diva-71316DOI: 10.1088/1742-6596/1085/2/022008ISI: 000467872200008OAI: oai:DiVA.org:ltu-71316DiVA, id: diva2:1258145
Konferens
18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2017)21–25 August 2017, Seattle, United States of America
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Tillgänglig från: 2018-10-24 Skapad: 2018-10-24 Senast uppdaterad: 2019-06-18Bibliografiskt granskad

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Albertsson, Kim

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