SciPost Submission Page
Sivers extraction with Neural Network
by I. P. Fernando, N. Newton, D. Seay & D. Keller
This Submission thread is now published as
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 |
| Specialties: |
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| Approaches: | Theoretical, Experimental, Computational, Phenomenological |
Abstract
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,
Ishara
Thanks for your comments, corrections, and suggestions. The revised manuscript is attached.
Thank you.
Best Regards,
Ishara
Published as SciPost Phys. Proc. 8, 035 (2022)