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Record W4399670203 · doi:10.1117/12.3028609

Characterization of the dark signal of the Solar Orbiter/Metis detectors

2024· article· en· W4399670203 on OpenAlexaboutno aff
M. Uslenghi, V. Andretta, Teriaca Luca, K. Heerlein, G. Nicolini, M. Pancrazzi, M. Romoli, Serena Farina, L. Abbo, A. Burtovoi, C. Casini, Yara De Leo, F. Frassati, G. Jerse, Federico Landini, G. Russano, C. Sasso, Roberto Susino

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsOrbiterMetisDetectorAstronomyOrbit (dynamics)PhysicsSIGNAL (programming language)Remote sensingAstrobiologyOpticsComputer scienceAerospace engineeringGeographyEngineering

Abstract

fetched live from OpenAlex

Metis, one of the instruments of the ESA mission Solar Orbiter (launched on 10 February 2020, from Cape Canaveral), is a coronagraph with 2 channels, capable of performing broadband polarization imaging in the visible range (580 to 640nm), and narrow-band imaging in UV (HI Lyman-α 121.6nm). It is equipped with two detectors based on CMOS APS sensors: the visible channel includes a custom CMOS sensor with direct illumination, while the UV channel is provided with an intensified camera, based on a Star-1000 rad-hard CMOS APS coupled via a 2:1 fiber optic taper to a single-stage Microchannel Plate intensifier coated with an opaque KBr photocathode and sealed with an entrance MgF2 window. Dark subtraction is a crucial step in the data reduction pipeline, thus requiring careful in-flight monitoring and characterization of the dark signal. Since it is not possible to directly acquire dark images with the visible detector, as the door of the instrument is not light-tight, an ad hoc procedure has been designed to estimate the correction to be applied. In the case of the UV detector, however, it is possible to acquire dark frames by turning off the intensifier. Due to small fluctuations occurring on the bias signal level even on short timescales, an algorithm has been developed to correct the dark matrix frame by frame.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.129

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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