Polarized DIS Structure Functions from Neural Networks

Journal Article · Wed Jun 13 00:00:00 EDT 2007 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2750812· OSTI ID:21063951

Del Debbio, L; Guffanti, A ^[1]; Piccione, A ^[2]; INFN Torino, Torino ^[3]

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1. School of Physics, University of Edinburgh, Edinburgh (United Kingdom)
2. Universita degli Studi di Torino, Torino (Italy)
3. Italy

We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g{sub 1}{sup p}(x,Q{sup 2})

• OSTI ID: 21063951
• Journal Information: AIP Conference Proceedings, Vol. 915, Issue 1; Conference: 17. international spin physics symposium, Kyoto (Japan), 2-7 Oct 2006; Other Information: DOI: 10.1063/1.2750812; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
CORRELATIONS
DEEP INELASTIC SCATTERING
ERRORS
FOUR MOMENTUM TRANSFER
NEURAL NETWORKS
PARTICLE KINEMATICS
PARTICLE STRUCTURE
POLARIZATION
PROBABILITY
STRUCTURE FUNCTIONS