Artificial Intelligence for the Electron Ion Collider (AI4EIC)
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Abstract (arXiv)
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 30 of its 135 references that are in the map. First posted on arXiv 17 Jul 2023.
Builds on
30 of 135 references.
References · 30
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
- 2022Generative Adversarial Networkscited by 5,603
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
- 2022Galactica: A Large Language Model for Sciencecited by 736
- 2017Masked Autoregressive Flow for Density Estimationcited by 680
- 2020A Brief Review of Domain Adaptationcited by 287
- 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 176
- 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
18 more
- 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
- 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
- 2021A Living Review of Machine Learning for Particle Physicscited by 77
- 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
- 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
- 2022On the Power of Foundation Modelscited by 37
- 2022Robust Multi-Objective Bayesian Optimization Under Input Noisecited by 36
- 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
- 2018Generative Models for Fast Calorimeter Simulation.LHCb casecited by 25
- 2022Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computingcited by 20
- 2019AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH casecited by 15
- 2019DeepRICH: Learning Deeply Cherenkov Detectorscited by 9
- 2022"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detectioncited by 7
- 2021A survey of machine learning-based physics event generationcited by 6
- 2021Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencodercited by 6
- 2022Design of Detectors at the Electron Ion Collider with Artificial Intelligencecited by 6
- 2022AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collidercited by 5
- 2022Artificial Intelligence for Imaging Cherenkov Detectors at the EICcited by 1
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.