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Sivers extraction with Neural Network

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

This Submission thread is now published as SciPost Phys. Proc. 8, 035 (2022)

Submission summary

As Contributors: Ishara Fernando
Preprint link: scipost_202107_00108v2
Date accepted: 2022-03-23
Date submitted: 2022-03-21 01:33
Submitted by: Fernando, Ishara
Submitted to: SciPost Physics Proceedings
Proceedings issue: DIS2021
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.

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

Author comments upon resubmission

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

Submission & Refereeing History

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

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Resubmission scipost_202107_00108v2 on 21 March 2022

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