Scalable AI-assisted workflow management for detector design optimization using distributed computing
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97 MATHEMATICS AND COMPUTING↗
Engineering topics
Suresh, K.
Publications and source records attributed to Suresh, K..
AI-assisted detector design for the EIC (AID(2)E)
Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.
97 MATHEMATICS AND COMPUTING↗
Search for axion-like particles through nuclear Primakoff production using the GlueX detector
We report on the results of the first search for the production of axion-like particles (ALPs) via Primakoff production on nuclear targets, γA → aA, in the “SRC-CT” experiment using the GlueX detector at Jefferson Lab. This search uses an integrated luminosity of 100 pb -1 nucleon on a 12 C target with a real photon beam of energies 6 < E γ < 10.8 GeV, and explores the mass region of 200 < m a < 450 MeV via the decay a → γγ. This mass range is between the π 0 and η meson masses, which enables the use of the measured η meson production rate to obtain absolute bounds on the ALP production with reduced sensitivity to experimental luminosity and detection efficiency. We find no evidence for an ALP, consistent with previous searches in the quoted mass range, and present limits on the effective photon coupling scale of $\mathscr{O}$(1 TeV -1 ). We further find that the ALP production limit we obtain is hindered by the peaking structure of the non-target-related dominant background the in GlueX spectrometer, which we treat by using data on 4 He to estimate and subtract it. We comment on how this search can be improved in a future higher-statistics dedicated measurement.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Artificial Intelligence for the Electron Ion Collider (AI4EIC)
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.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗