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000600225 1001_ $$0A.Butter.1$$aButter, Anja$$b0
000600225 1112_ $$aSnowmass 2021$$cSeattle$$d2022-07-17 - 2022-07-26$$wUnited States
000600225 245__ $$aMachine learning and LHC event generation
000600225 260__ $$aAmsterdam$$bSciPost Foundation$$c2023
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000600225 520__ $$aFirst-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
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000600225 7001_ $$0Alexander.Held.1$$aHeld, Alexander$$b15
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000600225 7001_ $$0Jessica.Howard.1$$aHoward, Jessica N.$$b17
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000600225 773__ $$0PERI:(DE-600)2886659-9$$a10.21468/SciPostPhys.14.4.079$$gVol. 14, no. 4, p. 079$$n4$$p079$$tSciPost physics$$v14$$x2542-4653$$y2023
000600225 7870_ $$0PUBDB-2022-01431$$aButter, Anja et.al.$$d2022$$iIsParent$$rDESY-22-043 ; arXiv:2203.07460$$tMachine Learning and LHC Event Generation
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