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Sökning: WFRF:(Hariharan S)

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  • Aguilar, J., et al. (författare)
  • Search for Leptonic CP Violation with the ESSnuSBplus Project
  • 2024
  • Ingår i: Letters in High Energy Physics. - : Andromeda Publishing And Academic Services LTD. - 2632-2714.
  • Tidskriftsartikel (refereegranskat)abstract
    • ESSνSB is a design study for a next-generation long-baseline neutrino experiment that aims at the precise measurement of the CP-violating phase, δCP, in the leptonic sector at the second oscillation maximum. The conceptual design report published from the first phase of the project showed that after 10 years of data taking, more than 70% of the possible δCP range will be covered with 5σ C.L. to reject the no-CP-violation hypothesis. The expected value of δCP precision is smaller than 8◦ for all δCP values. The next phase of the project, the ESSνSB+, aims at using the intense muon flux produced together with neutrinos to measure the neutrino-nucleus cross-section, the dominant term of the systematic uncertainty, in the energy range of 0.2–0.6 GeV, using a Low Energy neutrinos from STORed Muons (LEnuSTORM) and a Low Energy Monitored Neutrino Beam (LEMNB) facilities.
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  • Becker, Joel, et al. (författare)
  • Resource profile and user guide of the Polygenic Index Repository
  • 2021
  • Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
  • Tidskriftsartikel (refereegranskat)abstract
    • Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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