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Record W6912602921 · doi:10.5281/zenodo.5548464

NRC-HAA Cryogenic Radio Receiver Development

2021· article· en· W6912602921 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsCryostatSuperheterodyne receiverNoise temperatureAmplifierNoise (video)MicrowaveRadio receiver designW bandCryogenics

Abstract

fetched live from OpenAlex

The Radio Instrumentation Team (RIT) team at NRC Herzberg in Victoria, Canada, is developing a dual linear polarization, cryogenic radio astronomy receiver covering the frequency range of 30.5 to 50.5 GHz for the next generation Very Large Array (ngVLA) project. The specification of this receiver development is aligned with ngVLA Band 5 requirements. This receiver is designed for a noise temperature of less than 25 K over the bandwidth. The proposed receiver uses a vacuum vessel and a two-stage cryopump system for a cryogenic environment which provides 16 K and 70 K stages. The proposed receiver consists of a cryostat with a cooled feed horn, a turnstile OMT plus two integrated noise couplers for noise calibration, two mHEMT MMIC cryogenic low noise amplifiers with noise temperature lower than 14 K, IR filters, and a vacuum window to create a low-loss transmission of electromagnetic fields into the cryostat. The RIT team is also working on designing and developing various high-efficiency and wideband feed horns, vacuum windows, and OMTs. So far, a compact, low noise octave band OMT, multiple octave band feed horns, and a vacuum window covering the frequency range of 25-50 GHz have been designed. Most of the waveguide components designed and developed for the ngVLA Band 5 and octave band receiver are scalable to higher and lower frequency bands.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.013

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 head0.025
GPT teacher head0.221
Teacher spread0.196 · 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
GenreMethods

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
Published2021
Admission routes2
Has abstractyes

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