STI Publications - View Publication Form #19156
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Publication Information
| Title | A formalism for extracting track functions from jet measurements | ||||
| Abstract | The continued success of the jet substructure program will require wide spread use of tracking information to enable more precise measurements of a broader class of ob- servables. The recent reformulation of jet substructure in terms of energy correlators has enabled the incorporation of universal non-perturbative matrix elements, the so called ?track functions?, into jet substructure calculations. These advances make it timely to understand how these these universal non-perturbative functions can be extracted from hadron collider data, which is made complicated by the use jet algorithms. In this pa- per we derive a factorization theorem matching measurements of the track energy fraction in identified jets onto the universal non-perturbative track functions. This requires the introduction of new perturbatively calculable matching coefficients, which we term ?semi- inclusive semi-inclusive track fragmenting jet functions?. We calculate these new functions, and present phenomenological r | ||||
| Author(s) | Kyle Lee, Ian Moult, Felix Ringer, Wouter Waalewijn | ||||
| Publication Date | January 2024 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Theory & Comp Physics / THEORY CENTER / THEORY CENTER | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | Yes | ||||
| Supported by Jefferson Lab LDRD Funding | No |
Journal Article
| Journal Name | Journal of High Energy Physics |
| Refereed | No |
| Volume | 194 |
| Issue | |
| Page(s) |
Attachments/Datasets/DOI Link
| Document(s) |
trackfunctions.pdf
(STI Document)
JHEP01(2024)194.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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