Parton shower uncertainties in jet substructure analyses with deep neural networks
| العنوان: | Parton shower uncertainties in jet substructure analyses with deep neural networks |
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| المؤلفون: | Edmund Dawe, James Barnard, Matthew J. Dolan, Nina Rajcic |
| المصدر: | Physical Review D. 95 |
| بيانات النشر: | American Physical Society (APS), 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Quantum chromodynamics, Physics, Particle physics, Large Hadron Collider, Artificial neural network, 010308 nuclear & particles physics, FOS: Physical sciences, Jet (particle physics), 01 natural sciences, High Energy Physics - Experiment, 3. Good health, High Energy Physics - Phenomenology, High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology (hep-ph), 0103 physical sciences, High Energy Physics::Experiment, Network performance, 010306 general physics, Parton shower, Algorithm, Generator (mathematics), Event generator |
| الوصف: | Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels are known. However, training a network on a specific event generator may bias the network towards learning features associated with the approximations to QCD used in that generator which are not present in real data. We therefore investigate the effects of variations in the modelling of the parton shower on the performance of deep neural network taggers using jet images from hadronic W-bosons at the LHC, including detector-related effects. By investigating network performance on samples from the Pythia, Herwig and Sherpa generators, we find differences of up to fifty percent in background rejection for fixed signal efficiency. We also introduce and study a method, which we dub zooming, for implementing scale-invariance in neural network-based taggers. We find that this leads to an improvement in performance across a wide range of jet transverse momenta. Our results emphasise the importance gaining a detailed understanding what aspects of jet physics these methods are exploiting. Comment: 9 pages, 4 figures; v2: plots updated, references added |
| تدمد: | 2470-0029 2470-0010 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1bbc16365d7ec07a1f7bfed484e560aa https://doi.org/10.1103/physrevd.95.014018 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....1bbc16365d7ec07a1f7bfed484e560aa |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 24700029 24700010 |
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