New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering

Boglione, M.; Diefenthaler, M.; Dolan, S.; Gamberg, L.; Melnitchouk, W.; Pitonyak, D.; Prokudin, Alexei; Sato, N.; Scalyer, Z.

doi:10.1007/jhep04(2022)084

Title: New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering

Journal Article · Thu Apr 14 00:00:00 EDT 2022 · Journal of High Energy Physics (Online)

DOI:https://doi.org/10.1007/jhep04(2022)084· OSTI ID:1863933

Boglione, M. ^[1]; Diefenthaler, M. ^[2]; Dolan, S. ^[3]; Gamberg, L. ^[3]; Melnitchouk, W. ^[2]; Pitonyak, D. ^[4];

^[5]; Sato, N. ^[2]; Scalyer, Z. ^[6]

Univ. of Torino (Italy)
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Pennsylvania State Univ. Berks, Reading, PA (United States)
Lebanon Valley College, Annville, PA (United States)
Pennsylvania State Univ. Berks, Reading, PA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Temple Univ., Philadelphia, PA (United States)
Pennsylvania State Univ. Berks, Reading, PA (United States); Prepared for Flight, LLC, York, PA (United States)

We introduce a new phenomenological tool based on momentum region indicators to guide the analysis and interpretation of semi-inclusive deep-inelastic scattering measurements. The new tool, referred to as “affinity”, is devised to help visualize and quantify the proximity of any experimental kinematic bin to a particular hadron production region, such as that associated with transverse momentum dependent factorization. We apply the affinity estimator to existing HERMES and COMPASS data and expected data from Jefferson Lab and the future Electron-Ion Collider. We also provide an interactive notebook based on Machine Learning for fast evaluation of affinity.

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Research Organization:: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Pennsylvania State Univ. Berks, Reading, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP); National Science Foundation (NSF); European Union (EU)

Contributing Organization:: JAM Collaboration

Grant/Contract Number:: AC05-06OR23177; FG02-07ER41460; PHY-2011763; PHY-2012002; 824093

OSTI ID:: 1863933

Alternate ID(s):: OSTI ID: 1887742

Report Number(s):: JLAB-THY-22-3550; DOE/OR/23177-5409; arXiv:2201.12197; TRN: US2305676

Journal Information:: Journal of High Energy Physics (Online), Vol. 2022, Issue 4; ISSN 1029-8479

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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