Machine Learning in High Energy Physics Community White Paper

The main objectives of particle physics in the post-Higgs boson discovery era is to exploit the full physics potential of both the Large Hadron Collider (LHC) and its upgrade, the high luminosity LHC (HL-LHC), in addition to present and future neutrino experiments. The HL-LHC will deliver data from 100 times the luminosity compared to the LHC, bringing quantitatively and qualitatively new challenges due to event size, data volume, and complexity. The physics reach of the experiments will be limited by the physics performance of algorithms and computational resources. Machine learning (ML) applied to particle physics promises to provide improvements in both of these areas. Incorporating machine learning in particle physics workflows will require significant research and development over the next five years. Areas where significant improvements are needed include: - Physics performance of reconstruction and analysis algorithms; - Execution time of computationally expensive parts of event simulation, pattern recognition, and calibration; - Realtime implementation of machine learning algorithms; - Reduction of the data footprint with data compression, placement and access.