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R

R. Sack

Researcher at University of Münster

Publications -  19
Citations -  859

R. Sack is an academic researcher from University of Münster. The author has contributed to research in topics: KATRIN & Neutrino. The author has an hindex of 10, co-authored 19 publications receiving 525 citations.

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Journal ArticleDOI

Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN

M. Aker, +208 more
TL;DR: An upper limit of 1.1 eV (90% confidence level) is derived on the absolute mass scale of neutrinos on the Karlsruhe Tritium Neutrino experiment KATRIN, which improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation.
Posted ContentDOI

First direct neutrino-mass measurement with sub-eV sensitivity

M. Aker, +127 more
TL;DR: The second measurement campaign of the Karlsruhe Tritium Neutrino (KATRIN) experiment was reported in this paper, where the best fit to the spectral data yields an upper limit of (0.26\pm0.34)-,\mathrm{eV^4/c^4}$ at the 90% confidence level (CL).
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First operation of the KATRIN experiment with tritium

M. Aker, +170 more
TL;DR: The first operation of KATRIN with tritium neutrino was reported in 2018, and stable conditions over a time period of 13 days could be established as discussed by the authors.
Journal ArticleDOI

Bound on 3 +1 Active-Sterile Neutrino Mixing from the First Four-Week Science Run of KATRIN

M. Aker, +143 more
TL;DR: The light sterile neutrino search from the first four-week science run of the KATRIN experiment in 2019 is reported, and new limits supersede the Mainz results and improve the Troitsk bound for m_{4}^{2}<30 eV^{2}.
Journal ArticleDOI

Analysis methods for the first KATRIN neutrino-mass measurement

M. Aker, +128 more
- 13 Jan 2021 - 
TL;DR: Aker et al. as mentioned in this paper set an upper bound of 1.1 eV on the neutrino-mass scale at a 90% confidence level, based on a few weeks of running at a reduced source intensity and dominated by statistical uncertainty.