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S. Forte, L. Garrido, J. I. Latorre, and A. Piccione, Neural network parametrization of deep-inelastic structure functions
2002
Earlier work this paper cites.
J. Rojo and J. I. Latorre, Neural network parametrization of spectral functions from hadronic tau decays and determination of qcd vacuum condensates
2004
Earlier work this paper cites.
Springer Berlin Heidelberg, Berlin, Heidelberg, 2006
N. Hansen, The CMA Evolution Strategy: A Comparing Review · 2006
Earlier work this paper cites.
The NNPDF
Original
2009
Earlier work this paper cites.
The NNPDF
Original
2010
Earlier work this paper cites.
The NNPDF
Original
2011
Earlier work this paper cites.
R. D. Ball, V. Bertone, F. Cerutti, L. Del Debbio, S. Forte, et al., Reweighting and Unweighting of Parton Distributions and the LHC W lepton asymmetry data
Original
2012
Earlier work this paper cites.

Similar

NNPDF Collaboration
Original
2014
Cited alongside, same era.
V. Bertone, S. Carrazza, and J. Rojo, APFEL: A PDF Evolution Library with QED corrections
Original
2014
Cited alongside, same era.
S. Carrazza, J. I. Latorre, J. Rojo, and G. Watt, A compression algorithm for the combination of PDF sets
Original
2015
Cited alongside, same era.
J. Butterworth et al., PDF4LHC recommendations for LHC Run II
Original
2016
Cited alongside, same era.
R. D. Ball, E. R. Nocera, and J. Rojo, The asymptotic behaviour of parton distributions at small and large x x
Original
2016

Then

J. Carifio, J. Halverson, D. Krioukov, and B. D. Nelson, Machine Learning in the String Landscape
Original
2017
Later among the works it cites.
arXiv:1711.10840
Original
S. Carrazza, Machine learning challenges in theoretical HEP · 2017
Later among the works it cites.
V. Bertone, S. Carrazza, and N. P. Hartland, APFELgrid: a high performance tool for parton density determinations
Original
2017
Later among the works it cites.
A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel, A. Aurisano, K. Terao, and T. Wongjirad, Machine learning at the energy and intensity frontiers of particle physics
2018
Closest in time.
M. Paganini, L. de Oliveira, and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks

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2018

Machine Learning tools for global PDF fits

Rojo, Juan

Understand

The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector simulation among many others.

In this contribution, we review the machine learning tools used in the NNPDF family of global QCD analyses.
These include multi-layer feed-forward neural networks for the model-independent parametrisation of parton distributions and fragmentation functions, genetic and covariance matrix adaptation algorithms for training and optimisation, and closure testing for the systematic validation of the fitting methodology.

Built on

S. Forte, L. Garrido, J. I. Latorre, and A. Piccione, Neural network parametrization of deep-inelastic structure functions
2002
Earlier work this paper cites.
J. Rojo and J. I. Latorre, Neural network parametrization of spectral functions from hadronic tau decays and determination of qcd vacuum condensates
2004
Earlier work this paper cites.
Springer Berlin Heidelberg, Berlin, Heidelberg, 2006
N. Hansen, The CMA Evolution Strategy: A Comparing Review · 2006
Earlier work this paper cites.
The NNPDF
Original
2009
Earlier work this paper cites.
The NNPDF
Original
2010
Earlier work this paper cites.
The NNPDF
Original
2011
Earlier work this paper cites.
R. D. Ball, V. Bertone, F. Cerutti, L. Del Debbio, S. Forte, et al., Reweighting and Unweighting of Parton Distributions and the LHC W lepton asymmetry data
Original
2012
Earlier work this paper cites.