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.

Towards the compression of parton densities through machine learning algorithms / S. Carrazza, J. Latorre. - (2016 May 13). ((Intervento presentato al 51. convegno Rencontres de Moriond on QCD and High Energy Interactions tenutosi a La Thuile nel 2016.

Towards the compression of parton densities through machine learning algorithms

S. Carrazza;
2016-05-13

Abstract

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.
High Energy Physics - Phenomenology (hep-ph); High Energy Physics - Experiment (hep-ex); Data Analysis, Statistics and Probability (physics.data-an)
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
https://arxiv.org/abs/1605.04345
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/615417
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