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Record W3083237844 · doi:10.1103/physrevd.103.123508

Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments

2021· article· en· W3083237844 on OpenAlexafffund
Patrick Stöcker, Csaba Balázs, Sanjay Bloor, Torsten Bringmann, Tomás E. Gonzalo, Will Handley, Selim C. Hotinli, Cullan Howlett, Felix Kahlhoefer, Janina J. Renk, Pat Scott, Aaron C. Vincent, M. J. White

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsQueen's UniversityArthur B. McDonald-Canadian Astroparticle Physics Research InstitutePerimeter Institute
FundersAustralian Research CouncilOntario Ministry of Economic Development, Job Creation and TradeMinistero dello Sviluppo EconomicoRWTH Aachen UniversityVetenskapsrådetUniversity of CambridgeDeutsche ForschungsgemeinschaftCanada Foundation for InnovationPartnership for Advanced Computing in Europe AISBLInnovation, Science and Economic Development Canada
KeywordsNeutrinoPhysicsParticle physicsUpper and lower boundsLimit (mathematics)Nuclear physics

Abstract

fetched live from OpenAlex

We determine the upper limit on the mass of the lightest neutrino from the most robust recent cosmological and terrestrial data. Marginalizing over possible effective relativistic degrees of freedom at early times (${N}_{\mathrm{eff}}$) and assuming normal mass ordering, the mass of the lightest neutrino is less than 0.037 eV at 95% confidence; with inverted ordering, the bound is 0.042 eV. These results improve upon the strength and robustness of other recent limits and constrain the mass of the lightest neutrino to be barely larger than the largest mass splitting. We show the impacts of realistic mass models and different sources of ${N}_{\mathrm{eff}}$.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.006
Open science0.0030.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.357
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2021
Admission routes2
Has abstractyes

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