Towards the compression of parton densities through machine learning algorithms
Towards the compression of parton densities through machine learning algorithms [Cross-Listing] Stefano Carrazza, José I. Latorre ArXiv #: 1605.04345 (PDF, PS, ADS, Papers, Other) Comments: 4 pages, 4 figures, to appear in the proceedings of 50th Rencontres de Moriond, QCD and High Energy Interactions, La Thuile, Italy, March 2013 Originally posted by astro-ph from Unaffiliated on 05/16/2016 HEP-PH HEP-EX One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.
Link to Full Article: Towards the compression of parton densities through machine learning algorithms
Towards the compression of parton densities through machine learning algorithms
