mlsanityData to 14 Oct 2026

Explainable AI for High Energy Physics

arXiv 2206.06632 ↗First posted 14 Jun 2022hep-ex cs.LG physics.comp-phcited by 7

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Abstract (arXiv)

Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as textitblack boxes- whose inner workings to convey information and build the desired input-output relationship are often intractable. Explainable AI (xAI) methods can be useful in determining a neural model's relationship with data toward making it textitinterpretable by establishing a quantitative and tractable relationship between the input and the model's output. In this letter of interest, we explore the potential of using xAI methods in the context of problems in high energy physics.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

16 of 35 references.

References · 16

  1. 2014Going Deeper with Convolutionscited by 4,241
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  3. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  4. 2019Diagnosing and Enhancing VAE Modelscited by 193
  5. 2019Seeing What a GAN Cannot Generatecited by 148
  6. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
  7. 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
  8. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  9. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  10. 2016A Convolutional Neural Network Neutrino Event Classifiercited by 26
  11. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  12. 2020Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAscited by 23
4 more
  1. 2021Explaining machine-learned particle-flow reconstructioncited by 12
  2. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  3. 2021Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustabilitycited by 11
  4. 2022Interpretable machine learning in Physicscited by 8

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Cited by year

Citing papers per year: 2022: 5; 2023: 2.

7 papers.

  1. 2022FAIR for AI: An interdisciplinary and international community building perspectivecited by 13
  2. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  3. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 7
  4. 2022Do graph neural networks learn traditional jet substructure?cited by 4
  5. 2022FAIR AI Models in High Energy Physicscited by 4
  6. 2023Two-step interpretable modeling of Intensive Care Acquired Infectionscited by 0
  7. 2023LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Classcited by 0