Machine learning is an important 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.
Albertsson, Kim, et al. "Machine Learning in High Energy Physics Community White Paper." Journal of Physics. Conference Series, vol. 1085, no. 2, Sep. 2018. https://doi.org/10.1088/1742-6596/1085/2/022008
Albertsson, Kim, Altoe, Piero, Anderson, Dustin, et al., "Machine Learning in High Energy Physics Community White Paper," Journal of Physics. Conference Series 1085, no. 2 (2018), https://doi.org/10.1088/1742-6596/1085/2/022008
@article{osti_1463622,
author = {Albertsson, Kim and Altoe, Piero and Anderson, Dustin and Andrews, Michael and Araque Espinosa, Juan Pedro and Aurisano, Adam and Basara, Laurent and Bevan, Adrian and Bhimji, Wahid and Bonacorsi, Daniele and others},
title = {Machine Learning in High Energy Physics Community White Paper},
annote = {Machine learning is an important 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.},
doi = {10.1088/1742-6596/1085/2/022008},
url = {https://www.osti.gov/biblio/1463622},
journal = {Journal of Physics. Conference Series},
issn = {ISSN 1742-6588},
number = {2},
volume = {1085},
place = {United States},
publisher = {IOP Publishing},
year = {2018},
month = {09}}
Argonne National Laboratory (ANL), Argonne, IL (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 559, Issue 1https://doi.org/10.1016/j.nima.2005.11.166
Fiedler, Frank; Grohsjean, Alexander; Haefner, Petra
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Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3https://doi.org/10.1016/S0168-9002(03)01368-8