Artificial Intelligence/Machine Learning in Nuclear Medicine

Lee, Sang‐Won; Oh, Kyeong Taek; Choi, Yong; Yoo, Sun Kook; Yun, Mijin

doi:10.1007/978-3-031-00119-2_9

Cited by 1 publication

(1 citation statement)

References 56 publications

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“…In this context, AI offers state of the art solutions spanning from evolutionary algorithms to reinforcement learning in order to solve complex optimization problems in an efficient way by reducing the computing budget needed to converge to optimal solutions (see, e.g., [11][12][13]). According to the DOE Town Halls on AI for science [14] these new approaches can "revolutionize the way experimental nuclear physics is currently done".…”

Section: Introductionmentioning

confidence: 99%

Design of detectors at the electron ion collider with artificial intelligence

Fanelli

2022

J. Inst.

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Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The electron ion collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about ±35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.

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Section: Introductionmentioning

confidence: 99%

Design of detectors at the electron ion collider with artificial intelligence

Fanelli

2022

J. Inst.

View full text Add to dashboard Cite

show abstract

Artificial Intelligence/Machine Learning in Nuclear Medicine

Cited by 1 publication

References 56 publications

Design of detectors at the electron ion collider with artificial intelligence

Design of detectors at the electron ion collider with artificial intelligence

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