Abstract A test of lepton flavor universality in and decays, as well as a measurement of differential and integrated branching fractions of a nonresonant decay are presented. The analysis is made possible by a dedicated data set of proton-proton collisions at recorded in 2018, by the CMS experiment at the LHC, using a special high-rate data stream designed for collecting about 10 billion unbiased b hadron decays. The ratio of the branching fractions to is determined from the measured double ratio of these decays to the respective branching fractions of the with and decays, which allow for significant cancellation of systematic uncertainties. The ratio is measured in the range , whereqis the invariant mass of the lepton pair, and is found to be , in agreement with the standard model expectation . This measurement is limited by the statistical precision of the electron channel. The integrated branching fraction in the sameq2range, , is consistent with the present world-average value and has a comparable precision.
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Global system errors to simultaneously improve the identification of subsystems with mixed data Gaussian process regression
Abstract This paper explores the use of Gaussian process regression for system identification in control engineering. It introduces two novel approaches that utilize the data from a measured global system error. The paper demonstrates these approaches by identifying a simulated system with three subsystems, a one degree of freedom mass with two antagonist muscles. The first approach uses this whole-system error data alone, achieving accuracy on the same order of magnitude as subsystem-specific data ( of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as opposed to requiring a set of data descriptive of only a single subsystem. The second approach demonstrated in this paper mixes traditional subsystem-specific data with the whole system error data, achieving up to 98.71% model improvement.
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- Award ID(s):
- 1751821
- PAR ID:
- 10510764
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 5
- Issue:
- 2
- ISSN:
- 2632-2153
- Format(s):
- Medium: X Size: Article No. 025051
- Size(s):
- Article No. 025051
- Sponsoring Org:
- National Science Foundation
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