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Energetic neutron identification with pulse shape discrimination in pure CsI crystals

2022· article· en· W4226347920 on OpenAlexfundno aff
S. Longo, M. Khan, Sukeerthi Dharani, B. von Krosigk, T. Ferber

Bibliographic record

VenueJournal of Instrumentation · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
FundersDeutsches Elektronen-SynchrotronNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsScintillatorNeutronPhysicsNuclear physicsPulse (music)Neutron detectionMuonScintillationHadronNeutron temperatureDetectorOpticsAtomic physics

Abstract

fetched live from OpenAlex

Abstract Pulse shape discrimination with pure CsI scintillators is investigated as a method for separating energy deposits by energetic neutrons and photons at particle physics experiments. Using neutron data collected near the European XFEL XS1 beam window the pulse shape discrimination capabilities of pure CsI are studied and compared to CsI(Tl) using near-identical detector setups, which were operated in parallel. The inelastic interactions of 100 MeV neutrons are observed to produce a slower scintillation emission in pure CsI relative to energy deposits from cosmic muons. By employing a charge-ratio method for pulse shape characterization, pulse shape discrimination with pure CsI is shown to be effective for identifying energy deposits from neutrons vs. cosmic muons, however, pure CsI was not able resolve the specific type of neutron inelastic interactions as can be done with CsI(Tl). Using pulse shape discrimination, the rate of energetic neutron interactions in a pure CsI detector is measured as a function of time and shown to be correlated with the European XFEL beam power. The results demonstrate that pulse shape discrimination with pure CsI has significant potential to improve electromagnetic vs. hadronic shower identification at future particle physics experiments.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
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 studies0.0000.000
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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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