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Characterizing novel Indium Phosphide pad detectors with focused X-ray beams and laboratory tests

2024· article· en· W4404373729 on OpenAlexaffabout
E. R. Almazan, A. A. Affolder, Ian Dyckes, V. Fadeyev, M. Hance, M. B. Jadhav, Sungjoon Kim, Thomas Mccoy, J. Metcalfe, J. Nielsen, J. Ott, A. Poley, T. Shin, D. Sperlich, Anirudha V. Sumant

Bibliographic record

VenueJournal of Instrumentation · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsTRIUMF
Fundersnot available
KeywordsIndium phosphideDetectorMaterials scienceOpticsPhosphideIndiumOptoelectronicsEngineering physicsPhysicsMetallurgyMetal

Abstract

fetched live from OpenAlex

Abstract Future tracking systems in High Energy Physics experiments will require large instrumented areas with low radiation length. Crystalline silicon sensors have been used in tracking systems for decades, but are difficult to manufacture and costly to produce for large areas. We are exploring alternative sensor materials that are amenable to fast fabrication techniques used for thin film devices. Indium Phosphide pad sensors were fabricated at Argonne National Lab using commercially available InP:Fe 2-inch mono-crystal substrates. Current-voltage and capacitance-voltage characterizations were performed to study the basic operating characteristics of a group of sensors. Micro-focused X-ray beams at Canadian Light Source and Diamond Light Source were used to study the response to ionizing radiation, and characterize the uniformity of the response for several devices. Electrical test results showed a high degree of performance uniformity between the 48 tested devices. X-ray test beam results showed good performance uniformity within tested devices after accounting for spatially-local defects and edge fields. This motivates further studies into thin film devices for future tracking detectors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.231
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes2
Has abstractyes

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