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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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.011
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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