New avenue to the Parton Distribution Functions: Self-Organizing Maps

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dc.creatorCarnahan, J.
dc.creatorHonkanen, H.
dc.creatorLiuti, S.
dc.creatorLoitiere, Y.
dc.creatorReynolds, P. R.
dc.date2008-10-15
dc.date2008-11-02
dc.date.accessioned2026-07-07T13:09:44Z
dc.date.available2026-07-07T13:09:44Z
dc.descriptionNeural network algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations which provide an alternative to standard global fitting procedures. We propose a technique based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons. Our SOMs are trained on selections of stochastically generated PDF samples. The selection criterion for every optimization iteration is based on the features of the clustered PDFs. Our main goal is to provide a fitting procedure that, at variance with the standard neural network approaches, allows for an increased control of the systematic bias by enabling user interaction in the various stages of the process.
dc.description34 pages, 17 figures, minor revisions, 2 figures updated
dc.identifierhttps://arxiv.org/abs/0810.2598
dc.identifierhttp://arxiv.org/abs/0810.2598
dc.identifierPhys.Rev.D79:034022,2009
dc.identifierdoi:10.1103/PhysRevD.79.034022
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/228832
dc.subjectHigh Energy Physics - Phenomenology
dc.subjectComputational Engineering, Finance, and Science
dc.titleNew avenue to the Parton Distribution Functions: Self-Organizing Maps
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