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The DAMIC-M experiment: Status and first results

2023· article· en· W4307206780 on OpenAlexaff
I. J. Arnquist, N. Ávalos, P. Bailly, D. Baxter, X. Bertou, Mircea Bogdan, C. Bourgeois, Jim Brandt, Arnaud Cadiou, N. Castelló-Mor, Á. Chavarría, M.J. Filgueira Conde, Nicholas J. Corso, Jaime Gutiérrez, Julian Cuevas-Zepeda, A. Dastgheibi-Fard, C. De Dominicis, Olivier Deligny, R. Desani, M. Dhellot, Jean-Jacques Dormard, J. Duarte Campderros, E. Estrada, D. Florin, N. Gadola, R. Gaïor, J. González Sánchez, Todd W. Hossbach, M Huehn, Latifa Khalil, B. Kilminster, A. Lantero-Barreda, Ian Lawson, H. Lebbolo, S. Lee, P. Leray, A. Letessier‐Selvon, P. Loaiza, A. Lopez-Virto, David Martín, Ariel Matalon, Kellie McGuire, T. Milleto, P. Mitra, David M. Martin, Sravan Munagavalasa, D. Norcini, C.T. Overman, George K. Papadopoulos, S. Paul, David L. Peterson, A. Piers, Olivier Pochon, P. Privitera, Karthik Ramanathan, Denis Reynet, P. Robmann, Ryan Roehnelt, M. Settimo, Radomir Šmída, Ben Stillwell, Ryan M. Thomas, M. Traina, P. Vallerand, T. Van Wechel, I. Vila, R. Vilar Cortabitarte, A. Vollhardt, G. Warot, David Wolf, Rachana Yajur, J. P. Zopounidis

Bibliographic record

VenueSciPost Physics Proceedings · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsSnolab
FundersLawrence Berkeley National LaboratoryAgencia Estatal de InvestigaciónHorizon 2020Office of ScienceUniversity of ChicagoInstituto de Física de CantabriaU.S. Department of EnergyEuropean CommissionNational Science FoundationUniversity of WashingtonKavli FoundationAgencia Nacional de Promoción Científica y TecnológicaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDark matterPhysicsPixelOpticsRemote sensingParticle physicsGeography

Abstract

fetched live from OpenAlex

The DAMIC-M (DArk Matter In CCDs at Modane) experiment employs thick, fully depleted silicon charged-coupled devices (CCDs) to search for dark matter particles with a target exposure of 1 kg-year. A novel skipper readout implemented in the CCDs provides single electron resolution through multiple non-destructive measurements of the individual pixel charge, pushing the detection threshold to the eV-scale. DAMIC-M will advance by several orders of magnitude the exploration of the dark matter particle hypothesis, in particular of candidates pertaining to the so-called “hidden sector.” A prototype, the Low Background Chamber (LBC), with 20g of low background Skipper CCDs, has been recently installed at Laboratoire Souterrain de Modane and is currently taking data. We will report the status of the DAMIC-M experiment and first results obtained with LBC commissioning data.

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)0.015
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0050.003
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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 designObservational
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

Citations9
Published2023
Admission routes1
Has abstractyes

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Same venueSciPost Physics ProceedingsSame topicDark Matter and Cosmic PhenomenaFrench-language works237,207