MétaCan
Menu
Back to cohort

Fast neutron background characterization of the future Ricochet experiment at the ILL research nuclear reactor

2023· article· en· W4293568286 on OpenAlexaff
C. Augier, G. Baulieu, V. Belov, L. Bergé, J. Billard, Guillaume A. Brès, J-.L. Bret, A. Broniatowski, M. Calvo Gomez, A. Cazes, D. Chaize, M. Chapellier, Luke Chaplinsky, G. Chemin, R. Chen, J. Colas, M. De Jésus, P. de Marcillac, L. Dumoulin, O. Exshaw, S. Ferriol, E. Figueroa‐Feliciano, J.-B. Filippini, J. A. Formaggio, S. Fuard, J. Gascon, A. Giuliani, J. Goupy, C. Goy, C. Guérin, E. Guy, P. M. Harrington, Sarah Trowbridge Heine, S. A. Hertel, M. Heusch, Cyrus F. Hirjibehedin, Z. Hong, J.‐C. Ianigro, Yong Jin, J. Johnston, A. Juillard, D. V. Karaivanov, S. Kazarcev, J. Lamblin, H. Lattaud, M. Li, S. Marnieros, D. Mayer, Julien Minet, D. Misiak, Jacopo Mocellin, A. Monfardini, F. Mounier, W. D. Oliver, E. Olivieri, C. Oriol, P. K. Patel, E. Perbet, H. D. Pinckney, D. V. Poda, Dmitry Ponomarev, F. Rarbi, J. S. Réal, T. Redon, Aline Robert, S. Rozov, I. Rozova, T. Salagnac, V. Sanglard, B. Schmidt, E. Shevchik, T. Söldner, Juliana Stachurska, A. Stutz, L. Vagneron, W. Van De Pontseele, F. Vezzu, Steffen Georg Weber, L. A. Winslow, E. Yakushev, D. Zinatulina

Bibliographic record

VenueThe European Physical Journal C · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsUniversity of Toronto
FundersUniversité de LyonH2020 European Research CouncilAgence Nationale de la RechercheEuropean CommissionInstitut des Origines de LyonU.S. Department of EnergyHeising-Simons FoundationMinistry of Science and Higher Education of the Russian FederationNational Science Foundation
KeywordsNuclear physicsNeutronPhysicsNeutron temperatureNuclear dataNeutron detectionNuclear engineering

Abstract

fetched live from OpenAlex

Abstract The future Ricochet experiment aims at searching for new physics in the electroweak sector by providing a high precision measurement of the Coherent Elastic Neutrino-Nucleus Scattering (CENNS) process down to the sub-100 eV nuclear recoil energy range. The experiment will deploy a kg-scale low-energy-threshold detector array combining Ge and Zn target crystals 8.8 m away from the 58 MW research nuclear reactor core of the Institut Laue Langevin (ILL) in Grenoble, France. Currently, the Ricochet Collaboration is characterizing the backgrounds at its future experimental site in order to optimize the experiment’s shielding design. The most threatening background component, which cannot be actively rejected by particle identification, consists of keV-scale neutron-induced nuclear recoils. These initial fast neutrons are generated by the reactor core and surrounding experiments (reactogenics), and by the cosmic rays producing primary neutrons and muon-induced neutrons in the surrounding materials. In this paper, we present the Ricochet neutron background characterization using $$^3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>3</mml:mn> </mml:msup> </mml:math> He proportional counters which exhibit a high sensitivity to thermal, epithermal and fast neutrons. We compare these measurements to the Ricochet Geant4 simulations to validate our reactogenic and cosmogenic neutron background estimations. Eventually, we present our estimated neutron background for the future Ricochet experiment and the resulting CENNS detection significance. Our results show that depending on the effectiveness of the muon veto, we expect a total nuclear recoil background rate between 44 ± 3 and 9 ± 2 events/day/kg in the CENNS region of interest, i.e. between 50 eV and 1 keV. We therefore found that the Ricochet experiment should reach a statistical significance of 4.6 to 13.6 $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> for the detection of CENNS after one reactor cycle, when only the limiting neutron background is considered.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.048
GPT teacher head0.332
Teacher spread0.284 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations21
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe European Physical Journal CSame topicNeutrino Physics ResearchFrench-language works237,207