Machine Learning in High Energy Physics Community White Paper

Abstract

Machine learning has been applied to several problems in particle physics research, 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 for machine learning in particle physics. We detail 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.

arXiv commentEditors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

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Cited by in corpus (17)

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• Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics
• A deep neural network to search for new long-lived particles decaying to jets
• Boosted decision trees in the era of new physics: a smuon analysis case study
• GANs for generating EFT models
• Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
• Machine Learning based jet momentum reconstruction in heavy-ion collisions
• Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array
• On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case
• Uncertainties associated with GAN-generated datasets in high energy physics
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays
• Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
• Machine learning approach for the search of resonances with topological features at the Large Hadron Collider
• Search for anomalous quartic gauge couplings in the process ${\mu }^{+}{\mu }^{-}\to \bar{\nu }\nu \gamma \gamma$ with a nested local outlier factor
• Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
• Using holistic event information in the trigger
• Optimal event selection and categorization in high energy physics, Part 1: Signal discovery