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Record W4399663562 · doi:10.1117/12.3017859

Improving SPIRou sensitivity by reducing thermal background

2024· article· en· W4399663562 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSensitivity (control systems)Computer scienceThermalPhysicsEngineeringElectronic engineeringMeteorology

Abstract

fetched live from OpenAlex

SPIRou (SpectroPolarimètre Infra-Rouge in French), is a near-infrared, fiber-fed spectropolarimeter at the Canada-France- Hawaii Telescope (CFHT) which gives full spectral coverage from 0.98 to 2.35 μm with a resolving power of 70,000. To obtain an RV precision of ~1 m/s, SPIRou must measure and average the redshift of as many spectral lines as possible. The K-band contains many such lines making K-band sensitivity very important. Unfortunately, the original thermal background suppression design did not work as expected so that the background is over 5 times higher than desired reducing the K-band sensitivity by 2-3 magnitudes. Some of the background has been eliminated by cooling the fiber interface at the entrance to the spectrograph, but the fiber injection unit on the telescope still produces a large amount of background and is more difficult to cool. This paper will describe the cooling system installed on the SPIRou Cassegrain unit (fiber injection unit) and its performance.

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.

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: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.435

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 head0.007
GPT teacher head0.206
Teacher spread0.200 · 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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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