Physics – High Energy Physics – High Energy Physics - Phenomenology
Scientific paper
2008-11-03
PoS LC2008:022,2008
Physics
High Energy Physics
High Energy Physics - Phenomenology
7 pages, 2 figures. To appear in the proceedings of LIGHT CONE 2008, July 7-11, 2008, Mulhouse, France
Scientific paper
We propose a Parton Distribution Function (PDF) fitting technique which is based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are visualization algorithms based on competitive learning among spatially-ordered neurons. Our SOMs are trained with stochastically generated PDF samples. On every optimization iteration the PDFs are clustered on the SOM according to a user-defined feature and the most promising candidates are selected as a seed for the subsequent iteration. Our main goal is thus to provide a fitting procedure that, at variance with the global analyses and standard neural network approaches, allows for an increased control of the systematic bias by enabling user interaction in the various stages of the fitting process.
Honkanen Heli
Liuti Simonetta
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