MétaCan
Menu
Back to cohort
Record W2546110920 · doi:10.1109/nssmic.2013.6829700

Up down gamma discrimination using the imaging ratio method with CZT Gamma-ray detectors for In situ and remote sensing operations

2013· article· en· W2546110920 on OpenAlexaff
S. Nowicki, S. D. Hunter, Ann M. Parsons, Henry Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsRedlen Technologies (Canada)
Fundersnot available
KeywordsCadmium zinc tellurideDetectorGamma rayPhysicsSpectrometerOpticsImaging spectrometerGamma spectroscopySemiconductor detectorSensitivity (control systems)Particle detectorNuclear physics

Abstract

fetched live from OpenAlex

The neutron/gamma-ray group at NASA GSFC is currently developing the Imaging Gamma-ray Spectrometer (IGS), a next generation compact high-resolution high sensitivity gamma-ray imaging spectrometer. The innovative technology for IGS is the pixelated room-temperature semiconductor Cadmium Zinc Telluride (CZT) detector. This technology gives IGS the advantages of low mass and low power imaging over a large field of view and high spectroscopic resolution (1% FWHM at 662 keV). The imaging capabilities of pixelated CZT detectors can be used to determine the incoming directions of gamma rays. Therefore, it is possible to improve the sensitivity of the IGS to a planet below it by discriminating the gamma rays originating from above the IGS such as from the activation of the spacecraft. In this paper, the imaging ratio method is used to demonstrate that the gamma rays coming from a point source placed above the detectors can be eliminated from the energy spectrum thus improving the sensitivity of the detector to the gamma rays originating from below.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score0.432

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 head0.018
GPT teacher head0.268
Teacher spread0.250 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Semiconductor Detectors and MaterialsFrench-language works237,207