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Title: Machine Learning in High Energy Physics Community White Paper

Journal Article · · Journal of Physics. Conference Series
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  1. Lulea Univ. of Technology (Sweden)
  2. NVidia, Santa Clara, CA (United States)
  3. California Institute of Technology (CalTech), Pasadena, CA (United States)
  4. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  5. Lab. of Instrumentation and Experimental Particle Physics (LIP), Lisbon (Portugal)
  6. Univ. of Cincinnati, OH (United States)
  7. National Inst. for Nuclear Physics (INFN), Padova (Italy)
  8. Univ. of London (United Kingdom)
  9. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  10. National Inst. of Nuclear Physics (INFN), Bologna (Italy)
  11. European Organization for Nuclear Research (CERN), Geneva (Switzerland)
  12. Argonne National Lab. (ANL), Argonne, IL (United States)
  13. Univ. of Freiburg (Germany)
  14. New York Univ. (NYU), NY (United States)
  15. Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
  16. Duke Univ., Durham, NC (United States)
  17. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  18. RWTH Aachen Univ. (Germany)
  19. Univ. of Zurich (Switzerland)
  20. Univ. of Texas, Arlington, TX (United States)
  21. Southern Methodist Univ., Dallas, TX (United States)
  22. Ecole Polytechnique Federale Lausanne (Switzlerland)
  23. Univ. of Manchester (United Kingdom)
  24. Univ. of Cambridge (United Kingdom)
  25. Karlsruhe Inst. of Technology (KIT) (Germany)
  26. Univ. of Florida, Gainesville, FL (United States)
  27. Centre National de la Recherche Scientifique (CNRS), Caen (France)
  28. Univ. of Geneva (Switzerland)
  29. Louisiana Tech Univ., Ruston, LA (United States)
  30. Purdue Univ., West Lafayette, IN (United States)
  31. Univ. of Illinois at Urbana-Champaign, IL (United States)
  32. Max Planck Inst. for Physics, Heidelberg (Germany)
  33. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  34. Sound Cloud, Berlin (Germany)
  35. Univ. of Milan (Italy)
  36. Intel, Santa Clara, CA (United States)
  37. Brookhaven National Lab. (BNL), Upton, NY (United States)
  38. Univ. of Bristol (United Kingdom)
  39. Russian Academy of Sciences (RAS), Moscow (Russian Federation)
  40. Cornell Univ., Ithaca, NY (United States)
  41. Univ. of Notre Dame, IN (United States)
  42. Univ. of Melbourne (Australia)
  43. Univ. of California, Berkeley, CA (United States)
  44. National Inst. of Nuclear Physics (INFN), Milan (Italy)
  45. Vrije Univ., Brussels (Belgium)
  46. Brown Univ., Providence, RI (United States)
  47. Yale Univ., New Haven, CT (United States)
  48. Univ. of Alabama, Birmingham, AL (United States)
  49. Univ. of Massachusetts, Amherst, MA (United States)
  50. Princeton Univ., NJ (United States)
  51. Florida State Univ., Tallahassee, FL (United States)
  52. Indiana Univ., Bloomington, IN (United States)
  53. College of William and Mary, Williamsburg, VA (United States)
  54. Univ. of California, Santa Cruz, CA (United States)
  55. Rice Univ., Houston, TX (United States)
  56. Univ. of Paris-Sud, Orsay (France)
  57. Univ. of Oklahoma, Norman, OK (United States)
  58. Masaryk Univ., Brno (Czechia)
  59. Univ. of Glasgow, Scotland (United Kingdom)
  60. Radboud Univ., Nijmegen (Netherlands)
  61. Altair Engineering, Troy, MI (United States)
  62. Ohio Supercomputer Center (OSC), Columbus, OH (United States)
  63. Yandex School of Data Analysis (Russia)
  64. Technical Univ. of Kosice (Slovakia)
  65. Gangneung-Wonju National Univ., Gangwon-do (South Korea)
  66. Univ. of Rochester, NY (United States)
  67. Univ. of Barcelona (Spain)
  68. Univ. of Chicago, IL (United States)
  69. Univ. of Washington, Seattle, WA (United States)
  70. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  71. Chinese Academy of Sciences (CAS), Beijing (China)
  72. Univ. of Antioquia, Medellin (Columbia)
+ Show Author Affiliations

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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); European Research Council (ERC); COLCIENCIAS; Sostenibilidad-UdeA
Grant/Contract Number:
AC02-05CH11231; 111577657253; 724777
OSTI ID:
1616066
Journal Information:
Journal of Physics. Conference Series, Vol. 1085, Issue 2; Conference: 18.International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA (United States), 21-25 Aug 2017; ISSN 1742-6588
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 92 works
Citation information provided by
Web of Science

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APFELgrid: a high performance tool for parton density determinations text January 2016

Cited By (9)

Characterizing Magnetic Reconnection Regions Using Gaussian Mixture Models on Particle Velocity Distributions journal January 2020
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing journal October 2019
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network text January 2019
Energy Flow Networks: Deep Sets for Particle Jets text January 2018
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network text January 2019
Covariantizing phase space journal November 2020
On the coverage of neutralino dark matter in coannihilations at the upgraded LHC journal March 2020
DeepRICH: Learning Deeply Cherenkov Detectors text January 2019
MLaaS4HEP: Machine Learning as a Service for HEP preprint January 2020

Figures / Tables (3)

Figure 1(p. 19)figure Figure 1
Table 1(p. 19)table Table 1
Table 2(p. 21)table Table 2

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