Physics – High Energy Physics – High Energy Physics - Phenomenology
Scientific paper
2008-10-15
Phys.Rev.D79:034022,2009
Physics
High Energy Physics
High Energy Physics - Phenomenology
34 pages, 17 figures, minor revisions, 2 figures updated
Scientific paper
10.1103/PhysRevD.79.034022
Neural 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.
Carnahan J.
Honkanen Heli
Liuti Simonetta
Loitiere Y.
Reynolds P. R.
No associations
LandOfFree
New avenue to the Parton Distribution Functions: Self-Organizing Maps does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with New avenue to the Parton Distribution Functions: Self-Organizing Maps, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and New avenue to the Parton Distribution Functions: Self-Organizing Maps will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-302608