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Sivers extraction with Neural Network

by I. P. Fernando, N. Newton, D. Seay & D. Keller

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Submission summary

Authors (as registered SciPost users): Ishara Fernando
Submission information
Preprint Link: scipost_202107_00108v2  (pdf)
Date accepted: 2022-03-23
Date submitted: 2022-03-21 01:33
Submitted by: Fernando, Ishara
Submitted to: SciPost Physics Proceedings
Proceedings issue: 28th Annual Workshop on Deep-Inelastic Scattering (DIS) and Related Subjects (DIS2021)
Ontological classification
Academic field: Physics
  • Nuclear Physics - Experiment
  • Nuclear Physics - Theory
Approaches: Theoretical, Experimental, Computational, Phenomenological


Psuedo-data with simulated experimental errors can be generated to train an ensemble of Artificial Neural Networks (ANN) implemented on a regression to extract Transverse Momentum-dependent Distributions (TMDs). A preliminary analysis is presented on the reliability in extraction of the Sivers function imposed in the pseudo-data given the bounds on the experimental errors, data sparsity, and complexity of phase-space.

Author comments upon resubmission

Dear Editor,
Thanks for your comments, corrections, and suggestions. The revised manuscript is attached.
Thank you.
Best Regards,

Published as SciPost Phys. Proc. 8, 035 (2022)

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