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Direct Detection of keV-scale Electrons in Single-Photon Avalanche Diode-Based Detectors

2023· article· en· W4389667538 on OpenAlexaffabout
Maria Liubarska, Juan Pablo Yáñez, Madiha Tariq, F. Retière

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhotomultiplierDynodePhysicsPhotodetectorDetectorNeutrino detectorNeutrinoOpticsPhotonPhoton countingDiodeAvalanche photodiodeNanosecondSilicon photomultiplierOptoelectronicsScintillatorNuclear physicsLaserNeutrino oscillation

Abstract

fetched live from OpenAlex

Photomultiplier tubes (PMTs), despite being almost a century old, are still the most suitable photodetector for many astroparticle physics experiments, such as neutrino telescopes. One example of such a project is the Pacific Ocean Neutrino Experiment (P-ONE), a proposed deep-ocean neutrino telescope to be deployed off the coast of Canada that will monitor a few cubic kilometers of sea water with thousands of photodetectors. While the baseline design uses PMTs, incorporating single-photon avalanche diodes (SPADs), with their precise photon counting and sub-nanosecond time resolution, could greatly improve the detector pointing resolution. However, the requirements of large photosensitive areas with low single-photon noise levels limits their use. A proposed solution is to use a hybrid photomultiplier tube instead: a PMT where the dynode chain is substituted by a SPAD array. Used in this configuration, SPADs need to directly detect electrons with O(100 eV - 10 keV) energies. Here we present a systematic study of direct electron detection in a prototype SPAD and a commercially available array to guide the design of a SPAD tailored for a hybrid photodetector.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.212 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes2
Has abstractyes

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