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Record W3081278348 · doi:10.1063/5.0012432

An angle-scanned cryogenic Fabry–Pérot interferometer for far-infrared astronomy

2020· article· en· W3081278348 on OpenAlexafffund
Ian Veenendaal, David A. Naylor, Brad G. Gom, Adam J. Christiansen, Willem Jellema, Carolien Feenstra, M. Ridder, Martin Eggens, P. A. R. Ade

Bibliographic record

VenueReview of Scientific Instruments · 2020
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyCanada Foundation for InnovationAlberta Innovates - Technology Futures
KeywordsFabry–Pérot interferometerOpticsFar infraredInterferometryAstronomyInfrared astronomyPhysicsInfraredWavelength

Abstract

fetched live from OpenAlex

The sensitivity of state-of-the-art superconducting far-infrared detectors used in conjunction with cryogenically cooled space telescopes and instrumentation is such that spectroscopic observations are generally limited by photon noise from the astronomical source or by galactic foreground or zodiacal emission within the field-of-view. Therefore, an instrument design that restricts the spectral bandpass viewed by the detector must be employed. One method of achieving background limited, high resolution spectroscopy is to combine a high resolution component such as a Fabry-Pérot interferometer (FPI) with a lower resolution, post-dispersing system, such as a grating spectrometer, the latter serving to restrict the spectral bandpass. The resonant wavelength of an FPI is most often tuned by changing the spacing or medium between the parallel reflecting plates of the etalon. In this paper, we present a novel design for an FPI in which the wavelength is tuned by scanning the angle of incidence on a high refractive index etalon. This concept simplifies the cryomechanical design, actuation, and metrology. The first results from the realized instrument are presented and compared with theory. The effects on the spectral response as a function of the incident angle have been simulated and shown to agree well with the observation.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.038
GPT teacher head0.263
Teacher spread0.225 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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