Self-Organizing Maps and Parton Distribution Functions

Conference · Sun May 01 00:00:00 EDT 2011

DOI:https://doi.org/10.1142/9789814329569_0008· OSTI ID:1023158

K. Holcomb, Simonetta Liuti, D. Z. Perry

We present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly useful for an anaysis directed at extracting generalized parton distributions from data. We show quantitative results of our initial analysis of the parton distribution functions from inclusive deep inelastic scattering.

• Research Organization: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC05-06OR23177
• OSTI ID: 1023158
• Report Number(s): JLAB-THY-10-1213; DOE/OR/23177-1760; TRN: US201118%%463
• Resource Relation: Conference: 4th Workshop On Exclusive Reactions At High Momentum Transfer, 18-21 May 2010, Newport News, Virginia
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
DEEP INELASTIC SCATTERING
DISTRIBUTION FUNCTIONS
MOMENTUM TRANSFER
NEURAL NETWORKS