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Record W4390137248 · doi:10.48550/arxiv.2312.14110

Global Characterization of a Laser-Generated Neutron Source

2023· preprint· en· W4390137248 on OpenAlexaff
D. P. Higginson, Ronan Lelièvre, L. Vassura, M. Gugiu, M. Borghesi, L. A. Bernstein, D. L. Bleuel, B. L. Goldblum, A. Green, F. Hannachi, S. Kar, S. Kisyov, L. Quentin, Martin A. Schroer, M. Tarisien, O. Willi, P. Antici, F. Negoiţă, A. Allaoua, J. Fuchs

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersLawrence Berkeley National LaboratoryCentre National de la Recherche ScientifiqueInstitut de Radioprotection et de SÛreté NucléaireAgence Nationale de la RechercheLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsNeutronLaserInertial confinement fusionCharacterization (materials science)Neutron sourceNuclear physicsNeutron imagingOpticsTitan (rocket family)Neutron detectionDetectorPhysicsMaterials scienceNuclear engineering

Abstract

fetched live from OpenAlex

Laser-driven neutron sources are routinely produced by the interaction of laser-accelerated protons with a converter. They present complementary characteristics to those of conventional accelerator-based neutron sources (e.g. short pulse durations, enabling novel applications like radiography). We present here results from an experiment aimed at performing a global characterization of the neutrons produced using the Titan laser at the Jupiter Laser Facility (Livermore, USA), where protons were accelerated from 23 $μm$ thick plastic targets and directed onto a LiF converter to produce neutrons. For this purpose, several diagnostics were used to measure these neutron emissions, such as CR-39, activation foils, Time-of-Flight detectors and direct measurement of $^{7}$Be residual activity in the LiF converters. The use of these different, independently operating diagnostics enables comparison of the various measurements performed to provide a robust characterization. These measurements led to a neutron yield of $2.10^{9}$ neutrons per shot with a modest angular dependence, close to that simulated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

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 head0.041
GPT teacher head0.194
Teacher spread0.153 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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