Artificial Intelligence for imaging Cherenkov detectors at the EIC

Fanelli, C.; Mahmood, A. H.

doi:10.1088/1748-0221/17/07/c07011

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Cherenkov detectors constitute the backbone of PID at EIC [2]. When charged particles traverse the radiator medium-with appropriate refractive index and optical transparencyof a Cherenkov detector at speeds exceeding the phase velocity of light in that medium, they give rise to Cherenkov radiation, which emanates in a distinctive conical pattern.…”

Section: Background/motivationmentioning

confidence: 99%

AI4EIC Hackathon: PID with the ePIC dRICH

Fanelli,

Giroux,

McSpadden

et al. 2024

EPJ Web of Conf.

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The inaugural AI4EIC Hackathon unfolded as a high-point satellite event during the second AI4EIC Workshop at William & Mary. The workshop itself boasted over two hundred participants in a hybrid format and delved into the myriad applications of Artificial Intelligence and Machine Learning (AI/ML) for the Electron-Ion Collider (EIC). This workshop aimed to catalyze advancements in AI/ML with applications ranging from advancements in accelerator and detector technologies—highlighted by the ongoing work on the ePIC detector and potential development of a second detector for the EIC—to data analytics, reconstruction, and particle identification, as well as the synergies between theoretical and experimental research. Complementing the technical agenda was an enriched educational outreach program that featured tutorials from leading AI/ML experts representing academia, national laboratories, and industry. The hackathon, held on the final day, showcased international participation with ten teams from around the globe. Each team, comprising up to four members, focused on the dual-radiator Ring Imaging Cherenkov (dRICH) detector, an integral part of the particle identification (PID) system in ePIC. The data for the hackathon were generated using the ePIC software suite. While the hackathon presented questions of increasing complexity, its challenges were designed with deliberate simplifications to serve as a preliminary step toward the integration of machine learning and deep learning techniques in PID with the dRICH detector. This article encapsulates the key findings and insights gained from this unique experience.

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Section: Background/motivationmentioning

confidence: 99%

AI4EIC Hackathon: PID with the ePIC dRICH

Fanelli,

Giroux,

McSpadden

et al. 2024

EPJ Web of Conf.

View full text Add to dashboard Cite

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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

Allaire,

Ammendola,

Aschenauer

et al. 2024

Comput Softw Big Sci

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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.

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Artificial Intelligence for imaging Cherenkov detectors at the EIC

Cited by 2 publications

References 18 publications

AI4EIC Hackathon: PID with the ePIC dRICH

AI4EIC Hackathon: PID with the ePIC dRICH

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

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