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A deep neural network for simultaneous estimation of b jet energy and resolutionстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 27 мая 2021 г.
- Авторы: Baskakov A., Belyaev A.V., Boos E., Dubinin M., Dudko L., Ershov A.A., Gribushin A., Klyukhin V., Kodolova O., Lokhtin I., Obraztsov S., Petrushanko S., Savrin V., CMS Collaboration
- Журнал: Computing and Software for Big Science
- Том: 4
- Номер: 1
- Год издания: 2020
- Издательство: Springer Nature
- Местоположение издательства: Switzerland
- Первая страница: 10
- DOI: 10.1007/s41781-020-00041-z
- Аннотация: 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√= 13 TeV at the CERN LHC. The algorithm is trained on a large simulated sample of 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¯.
- Добавил в систему: Лохтин Игорь Петрович