STI Publications - View Publication Form #18453
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Publication Information
| Title | Is infrared-collinear safe information all you need for jet classification? | ||||
| Abstract | Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this discriminating power, and whether jet observables that are calculable in perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as sufficient inputs. In this article, we introduce a new classifier, Jet Flow Networks (JFNs), in an effort to address the question of whether IRC unsafe information provides additional discriminating power in jet classification. JFNs are permutation-invariant neural networks (deep sets) that take as input the kinematic information of reconstructed subjets. The subjet radius serves as a tunable hyperparameter, enabling the sensitivity to soft emissions and nonperturbative effects to be gradually increased as the subjet radius is decreased. We demonstrate the performance of JFNs for quark vs. g | ||||
| Author(s) | Dimitrios Athanasakos, Andrew Larkoski, James Mulligan, Mateusz Ploskon, Felix Ringer | ||||
| Publication Date | July 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 | Yes |
| Volume | 2024 |
| Issue | 07 |
| Page(s) | 257 |
Attachments/Datasets/DOI Link
| Document(s) |
2305.08979v1.pdf
(STI Document)
JHEP07(2024)257.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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