First direct neutrino-mass measurement with sub-eV sensitivity

Aker, M.; Böttcher, M.; Beglarian, A.; Behrens, J.; Berlëv, A. I.; Besserer, U.; Bieringer, B.; Block, F.; Bornschein, B.; Bornschein, L.; Brunst, T.; Ts, Caldwell; Carney, Rmd; Ll, Cascio; Chilingaryan, S.; Choi, W.; Debowski, K.; Deffert, M.; Descher, M.; Dd, Barrero; Pj, Doe; Dragouns, O; Drexlin, G.; Eitel, K.; Ellinger, Enrico; Engel, R.; Enomoto, S.; Felden, A.; Ja, Formaggio; Fm, Frankle; Gb, Franklin; Friedel, F.; Fulst, A.; Gauda, K.; Gil, W.; Glück, F.; Größle, Robin; Gumbsheimer, R.; Gupta, Rajiv; Höhn, Thomas; Hannen,; Haußmann, N.; Helbing, K.; Hickford, S.; Hiller, R.; Hillsheimer, D; Hinz, D.; Houdy, T.; Huber, A.; Jansen, A.; Karl, C.; Kellerer, F.; Kellerer, J.; Klein, Manuel; Köhler, Christoph; Köllenberger, L.; Kopmann, A.; Korzeczek, M.; Kovalı́k, A.; Krasch, B.; Krause, H.; Kunka, Norbert; Lasserre, T.; Tl, Le; Lebeda, O.; Lehnert, B.; Lokhov, Alexey; Machatschek, M.; Malcherek, E.; Mark, M.; Marsteller, A.; El, Martin; Melzer, C.; Menshikov, Alexander; Mertens, S.; Mostafa, J.; Müller, Klaus Peter; Niemes, S.; Oelpmann, P.; Parno, DS; Poon, Awp; Jml, Poyato; Priester, F.; Röllig, M.; Röttele, C.; Rgh, Robertson; Rodejohann, Werner; Rodenbeck, C.; Ryšavý, M.; Sack, R.; Sáenz, Alejandro; Schäfer, P.; As,; Schimpf, L.; Scholosser, K; Schlösser, M.; Schlüter, L.; Schneidewind, S.; Schrank, M.; Schulz, B.; Schwemmer, A.; Šefčík, M.; Sibille,; Siegmann, D.; Slezák, M.; Steidl, M.; Sturm, M.; Sun, Min; Tcherniakhovski, D.; Hh, Telle; La, Thorne; Thümmler, T.; Titov, N.; Tkachev, I.; Urban, K.; Valerius, K.; Vénos, D.; Apv, Hernandez; Weinheimer, C.; Welte, S.; Wendel, J.; Jf, Wilkerson; Wolf, J.; Wüstling, S.; Xu, W.; Yen, Y.-R.; Zadoroghny, S.; Zeller, G. P.

doi:10.21203/rs.3.rs-544214/v1

Cited by 18 publications

(21 citation statements)

References 76 publications

(72 reference statements)

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“…Background parameters and uncertainties not shown in table 1 are included without the use of a neural network as they are simple linear parameters. For the first measurement campaign, we arrive at m 2 ν = −1.14 +0.91 −1.10 eV 2 , consistent with the percent level with the updated value reported in [5]. When analyzing the second measurement campaign using a 12-ring segmentation for the detector, we find m 2 ν = 0.27 +0.33 −0.33 eV 2 which is within the range of values reported by the analysis teams in [5] where the individual results can differ by around 0.01 eV 2 .…”

Section: Analysis Of the First Two Measurement Campaignssupporting

confidence: 88%

“…The KATRIN collaboration follows multiple approaches to infer the value, uncertainty, and thus confidence interval of the neutrino mass [5,11,12]. In the following, we will describe why each approach requires excessive computational power and would thus benefit from a fast calculation of the model.…”

Section: Analysis Methods and Computing Requirementsmentioning

confidence: 99%

“…Both bias (−3 × 10 −4 eV 2 ) and width (4 × 10 −4 eV 2 ) are below the 1 × 10 −3 eV 2 level. Right: Comparison of our analysis of the first two measurement campaigns using the neural network (blue) with the results published in [5,11]. The gray dotted line splits the first measurement campaign (top) and the second one (bottom).…”

Section: Asimov Cross-fitmentioning

confidence: 96%

“…As a first step, we reproduce the published results of the first two measurement campaigns [5,11] using the neural network and the nuisance parameter approach. We include all systematic uncertainties in the official analysis except for an uncertainty on the molecular final states and the source activity fluctuation as these are not parameterized and will be treated differently in future analyses.…”

Section: Analysis Of the First Two Measurement Campaignsmentioning

confidence: 99%

“…KATRIN is currently the leading experiment using the kinematics of β-decay for the measurement of the absolute neutrino mass scale. Based on the first two data taking campaigns, KATRIN set a limit of m ν < 0.8 eV (90 % CL) [5]. With the full data set of 1000 days, KATRIN targets a sensitivity close to 0.2 eV at 90 % CL [4].…”

Section: The Katrin Physics Modelmentioning

confidence: 99%

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Fast and precise model calculation for KATRIN using a neural network

Karl,

Eller,

Mertens

2022

Preprint

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We present a fast and precise method to approximate the physics model of the Karlsruhe Tritium Neutrino (KATRIN) experiment using a neural network. KATRIN is designed to measure the effective electron anti-neutrino mass m ν using the kinematics of β-decay with a sensitivity of 200 meV at 90 % confidence level. To achieve this goal, a highly accurate model prediction with relative errors below the 10 −4 -level is required. Using the regular numerical model for the analysis of the final KATRIN dataset is computationally extremely costly or requires approximations to decrease the computation time. Our solution to reduce the computational requirements is to train a neural network to learn the predicted β-spectrum and its dependence on all relevant input parameters. This results in a speed-up of the calculation by about three orders of magnitude, while meeting the stringent accuracy requirements of KATRIN.

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Section: Analysis Of the First Two Measurement Campaignssupporting

confidence: 88%

Section: Analysis Methods and Computing Requirementsmentioning

confidence: 99%

Section: Asimov Cross-fitmentioning

confidence: 96%

Section: Analysis Of the First Two Measurement Campaignsmentioning

confidence: 99%

Section: The Katrin Physics Modelmentioning

confidence: 99%

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Fast and precise model calculation for KATRIN using a neural network

Karl,

Eller,

Mertens

2022

Preprint

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Status and perspectives of neutrino physics

Athar

Barwick

Brunner

et al. 2022

Progress in Particle and Nuclear Physics

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Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

Avilés¹,

Banerjee²,

Niz³

et al. 2021

J. Cosmol. Astropart. Phys.

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We introduce an Eulerian Perturbation Theory to study the clustering of tracers for cosmologies in the presence of massive neutrinos. Our approach is based on mapping recently-obtained Lagrangian Perturbation Theory results to the Eulerian framework. We add Effective Field Theory counterterms, IR-resummations and a biasing scheme to compute the one-loop redshift-space power spectrum. To assess our predictions, we compare the power spectrum multipoles against synthetic halo catalogues from the QUIJOTE simulations, finding excellent agreement on scales k ≲ 0.25 h Mpc-1. One can obtain the same fitting accuracy using higher wave-numbers, but then the theory fails to give a correct estimation of the linear bias parameter. We further discuss the implications for the tree-level bispectrum. Finally, calculating loop corrections is computationally costly, hence we derive an accurate approximation wherein we retain only the main features of the kernels, as produced by changes to the growth rate. As a result, we show how FFTLog methods can be used to further accelerate the loop computations with these reduced kernels.

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First direct neutrino-mass measurement with sub-eV sensitivity

Cited by 18 publications

References 76 publications

Fast and precise model calculation for KATRIN using a neural network

Fast and precise model calculation for KATRIN using a neural network

Status and perspectives of neutrino physics

Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

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