Neural Networks for Impact Parameter Determination

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dc.creatorBass, S. A.
dc.creatorBischoff, A.
dc.creatorMaruhn, J. A.
dc.creatorStoecker, H.
dc.creatorGreiner, W.
dc.date1996-01-17
dc.date.accessioned2026-07-07T11:15:37Z
dc.date.available2026-07-07T11:15:37Z
dc.descriptionAn accurate impact parameter determination in a heavy ion collision is crucial for almost all further analysis. The capabilities of an artificial neural network are investigated to that respect. A novel input generation for the network is proposed, namely the transverse and longitudinal momentum distribution of all outgoing (or actually detectable) particles. The neural network approach yields an improvement in performance of a factor of two as compared to classical techniques. To achieve this improvement simple network architectures and a 5 by 5 input grid in (p_t,p_z) space are sufficient.
dc.descriptionPhys. Rev. C in print. Postscript-file also available at http://www.th.physik.uni-frankfurt.de/~bass/pub.html
dc.identifierhttps://arxiv.org/abs/nucl-th/9601024
dc.identifierhttp://arxiv.org/abs/nucl-th/9601024
dc.identifierPhys.Rev.C53:2358-2363,1996
dc.identifierdoi:10.1103/PhysRevC.53.2358
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/192445
dc.subjectNuclear Theory
dc.titleNeural Networks for Impact Parameter Determination
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