Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors
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arXiv
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| Natura: | Preprint |
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2025
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| author | Kvapil, J. Borca-Tasciuc, G. Bossi, H. Chen, K. Chen, Y. Morales, Y. Corrales Da Costa, H. Da Silva, C. Dean, C. Durham, J. Fu, S. Hao, C. Harris, P. Hen, O. Jheng, H. Lee, Y. Li, P. Li, X. Lin, Y. Liu, M. X. Loncar, V. Mitrevski, J. P. Olvera, A. Purschke, M. L. Renck, J. S. Roland, G. Schambach, J. Shi, Z. Tran, N. Wuerfel, N. Xu, B. Yu, D. Zhang, H. |
| author_facet | Kvapil, J. Borca-Tasciuc, G. Bossi, H. Chen, K. Chen, Y. Morales, Y. Corrales Da Costa, H. Da Silva, C. Dean, C. Durham, J. Fu, S. Hao, C. Harris, P. Hen, O. Jheng, H. Lee, Y. Li, P. Li, X. Lin, Y. Liu, M. X. Loncar, V. Mitrevski, J. P. Olvera, A. Purschke, M. L. Renck, J. S. Roland, G. Schambach, J. Shi, Z. Tran, N. Wuerfel, N. Xu, B. Yu, D. Zhang, H. |
| contents | This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors Kvapil, J. Borca-Tasciuc, G. Bossi, H. Chen, K. Chen, Y. Morales, Y. Corrales Da Costa, H. Da Silva, C. Dean, C. Durham, J. Fu, S. Hao, C. Harris, P. Hen, O. Jheng, H. Lee, Y. Li, P. Li, X. Lin, Y. Liu, M. X. Loncar, V. Mitrevski, J. P. Olvera, A. Purschke, M. L. Renck, J. S. Roland, G. Schambach, J. Shi, Z. Tran, N. Wuerfel, N. Xu, B. Yu, D. Zhang, H. Instrumentation and Detectors Machine Learning High Energy Physics - Experiment Nuclear Experiment This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines. |
| title | Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors |
| topic | Instrumentation and Detectors Machine Learning High Energy Physics - Experiment Nuclear Experiment |
| url | https://arxiv.org/abs/2501.04845 |