Neural network parametrization of deep inelastic structure functions

By S. Forte, Lluís Garrido, José I. Latorre. 2002

193 citations recorded by the source corpus.

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F2 of the proton and deuteron, and a precision determination of the isotriplet combination F2[p?d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.

Subfields: Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c18b0e98d27ed8341c27e2116183c7b261cb7314

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Citations in the corpus

The Structure of the Proton in the LHC Precision Era (2017, methodology): “The NNPDF methodology was first presented in [131], where it was used to produce a neural network based determination of the proton, deuteron and non–singlet DIS structure functions from the NMC and BCDMS fixed target data.”
Heavy-flavour and quarkonium production in the LHC era: from proton–proton to heavy-ion collisions (2015, methodology): “This approach has also been applied to DIS structure functions and is used in the global analyses of PDFs by the NNPDF Collaboration [42,43].”
Parton distributions for the LHC run II (2014, background): “It is interesting to observe that NMC proton data, which are known to have internal inconsistencies [3], as well as other (2)We notice that the ATLAS W pT data, which are included in the NNPDF3.”
Progress in the Determination of the Partonic Structure of the Proton (2013, methodology): “An alternative approach to PDF fitting was proposed in Reference [29], and eventually led the NNPDF collaboration to produce a first PDF set based on DIS data [30] and then a PDF set from a global fit [31].”