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Record W4412671439 · doi:10.1038/s41598-025-05682-x

Laser-driven proton sources for efficient radiation testing

2025· article· en· W4412671439 on OpenAlexafffund
Beatrice D’Orsi, Corrado Altomare, A. Ampollini, Maria Denise Astorino, G. Bazzano, Elias Catrix, Alessia Cemmi, A. Colangeli, Ilaria Di Sarcina, Ronan Lelièvre, S. Loreti, S. Fourmaux, J. Fuchs, Paolo Nenzi, G. Pagano, Fabio Panza, C. Ronsivalle, J. Scifo, Simon Vallières, P. Antici

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsHydro-QuébecInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaAgenzia Spaziale ItalianaCompute Canada
KeywordsProtonLaserRadiationComputer sciencePhysicsNuclear physicsOptics

Abstract

fetched live from OpenAlex

Several fields such as particle physics, space exploration, and high-energy physics, use Commercial Off-The-Shelf (COTS) electronic components in a high-radiation operative environment. These operating conditions can cause significant damage to electronic circuits, affecting their operational features and thus the reliability of the entire facility. Qualifying and characterizing these components against radiation is essential to ensure their proper functioning in harsh conditions. This study investigates the effect of stress-testing electronic components used in high-radiation environments with various types of radiation sources. The components were exposed to gamma rays, laser-driven protons, conventionally accelerated protons, and neutrons, analyzing the devices parameters after different irradiation conditions. The results indicate significant degradation in electrical performance due to radiation-induced defects and significant variations of the effects at same dose delivery. We show that laser-driven proton irradiation achieves equivalent stress-testing with doses two orders of magnitude lower and much quicker than other radiation sources, demonstrating a much higher stress-testing efficiency.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.374

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 studies0.0000.000
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 head0.013
GPT teacher head0.271
Teacher spread0.257 · 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 designNot applicable
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

Citations3
Published2025
Admission routes2
Has abstractyes

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