A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution; Computing and Software for Big Science; Vol. 2020, iss. 4
| Parent link: | Computing and Software for Big Science Vol. 2020, iss. 4.— 2020.— [10, 20 p.] |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , , , |
| Sumario: | Title screen We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of √s =13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb−1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to bb¯. |
| Lenguaje: | inglés |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://doi.org/10.1007/s41781-020-00041-z |
| Formato: | Electrónico Capítulo de libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666334 |
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| 200 | 1 | |a A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution |f A. M. Sirunyan, A. R. Tumasyan, W. Adam [et al.] | |
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| 300 | |a Title screen | ||
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| 330 | |a We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of √s =13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb−1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to bb¯. | ||
| 461 | |t Computing and Software for Big Science | ||
| 463 | |t Vol. 2020, iss. 4 |v [10, 20 p.] |d 2020 | ||
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| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a CMS | |
| 610 | 1 | |a b jets | |
| 610 | 1 | |a Higgs boson | |
| 610 | 1 | |a jet energy | |
| 610 | 1 | |a jet resolution | |
| 610 | 1 | |a deep learning | |
| 701 | 1 | |a Sirunyan |b A. M. | |
| 701 | 1 | |a Tumasyan |b A. R. | |
| 701 | 1 | |a Adam |b W. |g Wolfgang | |
| 701 | 1 | |a Ambrogi |b F. |g Federico | |
| 701 | 1 | |a Tyurin |b N. E. |g Nikolay Evgenjevich | |
| 701 | 1 | |a Babaev |b A. A. |c physicist |c engineer-issledovatelskogo Polytechnic University, candidate of physical and mathematical Sciences |f 1981- |g Anton Anatoljevich |3 (RuTPU)RU\TPU\pers\35154 |9 18420 | |
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