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Record W6948116916 · doi:10.48550/arxiv.0806.0776

Technical improvements and performances of SpIOMM: an imaging Fourier transform spectrometer for astronomy

2008· preprint· en· W6948116916 on OpenAlexaboutno aff

Bibliographic record

VenuearXiv (Cornell University) · 2008
Typepreprint
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsnot available
Fundersnot available
KeywordsTelescopeSpectrometerMetrologyInstrumentation (computer programming)SkyNear-infrared spectroscopyInterferometryTransmission (telecommunications)Observatory

Abstract

fetched live from OpenAlex

We present the most recent technical improvements on SpIOMM, an Imaging Fourier Transform Spectrometer (IFTS) attached to the 1.6 telescope of the Mont Mégantic Observatory. The recent development of SpIOMM demonstrates that the concept of IFTS for ground telescopes is a promising astronomical 3D spectroscopy technique for multi-object spectroscopy and multi-band imaging. SpIOMM has been developed through a collaboration between Université Laval and the industry (ABB Bomem). It is designed for optical observations from the near UV (350 nm) to the near IR (850 nm) with variable spectral resolution. The circular FOV of the instrument covers 12 arcmin in diameter. We have recently improved the servo system algorithm which now controls the mirror displacement and alignment at a rate of ~7000Hz. Hardware improvements to the servo and the metrology system will be described along with their impacts on performance in the laboratory and in observing conditions. The instrument has successfully been operated at the 1.6 meter telescope this year using the revised control systems and acquired several datacubes. We will discuss some issues regarding the sensitivity to environmental conditions implied by the use of such an instrument. An overview of the datacube reduction procedure will show some solutions proposed for observational problems encountered that affect the quality of the data such as sky transmission variations, wind, changing gravity vector and temperature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.494
Threshold uncertainty score0.906

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 head0.029
GPT teacher head0.177
Teacher spread0.148 · 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 designSimulation or modeling
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
Published2008
Admission routes1
Has abstractyes

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