mlsanityData to 7 Oct 2026

Sequence-based Machine Learning Models in Jet Physics

arXiv 2102.06128 ↗First posted 9 Feb 2021physics.data-an cs.LG hep-excited by 3

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Abstract (arXiv)

Sequence-based modeling broadly refers to algorithms that act on data that is represented as an ordered set of input elements. In particular, Machine Learning algorithms with sequences as inputs have seen successfull applications to important problems, such as Natural Language Processing (NLP) and speech signal modeling. The usage this class of models in collider physics leverages their ability to act on data with variable sequence lengths, such as constituents inside a jet. In this document, we explore the application of Recurrent Neural Networks (RNNs) and other sequence-based neural network architectures to classify jets, regress jet-related quantities and to build a physics-inspired jet representation, in connection to jet clustering algorithms. In addition, alternatives to sequential data representations are briefly discussed.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

13 of 43 references.

References · 13

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1 more
  1. 2018Jet Charge and Machine Learningcited by 5

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2022Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4mlcited by 14
  2. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  3. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3