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First Measurement of the Nuclear-Recoil Ionization Yield in Silicon at 100 eV

2023· article· en· W4323547418 on OpenAlexafffund
M. F. Albakry, I. Alkhatib, Del Alonso, D. W. P. Amaral, T. Aralis, T. Aramaki, I. J. Arnquist, I. Ataee Langroudy, E. Azadbakht, S. Banik, C. Bathurst, R. Bhattacharyya, P. L. Brink, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, C. Cartaro, D. G. Cerdeño, Y.-Y. Chang, M. Chaudhuri, R. Chen, N. Chott, J. Cooley, H. Coombes, Jacqueline Corbett, P. Cushman, S. Das, F. De Brienne, Martín de los Rios, Sukeerthi Dharani, M. L. di Vacri, M. D. Diamond, M. Elwan, E. Fascione, E. Figueroa‐Feliciano, C. W. Fink, K. Fouts, M. Fritts, G. Gerbier, R. Germond, M. Ghaith, S. R. Golwala, J. Hall, S. A. S. Harms, N. Hassan, B. A. Hines, Z. Hong, E. W. Hoppe, L. Hsu, M. E. Huber, V. Iyer, Vipul Kashyap, M. Kelsey, A. Kubik, N. A. Kurinsky, M. Lee, M. Litke, J. Liu, Y. Liu, B. Loer, E. Lopez Asamar, P. Lukens, D. B. MacFarlane, R. Mahapatra, N. Mast, A. Mayer, H. Meyer Zu Theenhausen, Eva Michaud, E. Michielin, N. Mirabolfathi, B. Mohanty, B. Nebolsky, J. K. Nelson, H. Neog, V. Novati, J. L. Orrell, M. D. Osborne, S. M. Oser, W. A. Page, L. Pandey, S. Pandey, R. Partridge, D. S. Pedreros, L. Perna, R. Podviianiuk, F. Ponce, S. S. Poudel, A. Pradeep, M. Pyle, W. Rau, E. Reid, R. Ren, T. Reynolds, Evan Tanner, A. Roberts, Alan Robinson, T. Saab, D. Sadek, B. Sadoulet, S. P. Sahoo, I. Saikia, J. Sander, Amirmohammad Sattari, B. Schmidt, R. W. Schnee, S. Scorza, B. Serfass, D. J. Sincavage, P. Sinervo, Z. Speaks, John Street, Hengxin Sun, G. D. Terry, F. K. Thasrawala, D. Toback, R. Underwood, S. Verma, A. N. Villano, B. von Krosigk, S. L. Watkins, O. Wen, Z. Williams, M. J. Wilson, J. Winchell, K. Wykoff, S. Yellin, Betty Young, T. C. Yu, B. Zatschler, S. Zatschler, A. Zaytsev, Andrew Zeolla, E. Zhang, Liang Zheng, Y. Zheng, A. Zuniga, Peng An, P. S. Barbeau, S. Hedges, Liqian Li, J. Runge

Bibliographic record

VenuePhysical Review Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsLaurentian UniversityUniversité de MontréalQueen's UniversityUniversity of British ColumbiaUniversity of TorontoTRIUMF
FundersPacific Northwest National LaboratoryDepartment of Atomic Energy, Government of IndiaAlliance de recherche numérique du CanadaCanada First Research Excellence FundDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaStanford UniversityBattelleDepartment of Science and Technology, Ministry of Science and Technology, IndiaSLAC National Accelerator LaboratoryFermilabU.S. Department of EnergyNational Science Foundation
KeywordsRecoilIonizationSiliconYield (engineering)Atomic physicsPhysicsNeutronDetectorNuclear physicsBeam (structure)Materials scienceOpticsIonOptoelectronics

Abstract

fetched live from OpenAlex

We measured the nuclear-recoil ionization yield in silicon with a cryogenic phonon-sensitive gram-scale detector. Neutrons from a monoenergetic beam scatter off of the silicon nuclei at angles corresponding to energy depositions from 4 keV down to 100 eV, the lowest energy probed so far. The results show no sign of an ionization production threshold above 100 eV. These results call for further investigation of the ionization yield theory and a comprehensive determination of the detector response function at energies below the keV scale.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.257
Teacher spread0.222 · 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 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

Citations20
Published2023
Admission routes2
Has abstractyes

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