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Record W4306247412 · doi:10.1016/j.pss.2022.105580

In-flight radiometric calibration of the ExoMars TGO Colour and Stereo Surface Imaging System

2022· article· en· W4306247412 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlanetary and Space Science · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsWestern University
FundersCanadian Space AgencyUniversity of BernNatural Sciences and Engineering Research Council of CanadaUniversity of ArizonaNational Aeronautics and Space Administration
KeywordsCalibrationRadiometric calibrationRemote sensingOrbiterStereo imagingRadiometric datingGeologyDetectorMartian surfaceMars Exploration ProgramOpticsComputer scienceMartianPhysicsAstrobiologyAstronomy

Abstract

fetched live from OpenAlex

The Colour and Stereo Surface Science Imaging System (CaSSIS) of the ExoMars Trace Gas Orbiter returns on average twenty images per day of the Martian surface, most of them in 3 or 4 colours and some of them in stereo. CaSSIS uses a push-frame approach to acquire colour images, with four bandpass filters deposited directly above the sensor and an imaging cadence synchronized with the ground track velocity to cover the imaged area with tens of small, partially overlapping images. These “framelets” are later map-projected and mosaicked to build the final image. This approach offers both advantages and challenges in terms of radiometric calibration. While the collection of dark and flatfield frames is considerably enhanced by the frequent and fast acquisition of tens of successive images, mosaics assembled from the adjacent framelets highlight the straylight and changes in the bias of the detector. Both issues have been identified on CaSSIS images, with low intensities overall (up to a few %), but sufficient to generate prominent artefacts on the final assembled colour images. We have therefore developed methods to correct these artefacts that are now included into the radiometric calibration pipeline. We detail here the different steps of the calibration procedure and the generation of the products used for calibration, and discuss the efficacy of the corrections. The relative uncertainties on the bias and flatfield frames are low, of the order of 0.2 and 0.1%, respectively. The uncertainty on the absolute radiometric calibration is of 3%, which is quite low for such an instrument. The straylight adds an estimated ∼1% error to the absolute calibration. The residuals after corrections of the straylight and bias offsets are of the order of a few DNs to tens of DNs. As CaSSIS can observe the Martian surface in challenging illumination conditions to provide unique views of the surface at early and late local solar time, residuals from the straylight correction can become noticeable when the absolute signal is very low. As they appear at the level of the noise in very low illumination images, these residuals do not limit the scientific exploitation of the data. For most of the dataset, as the signal in well-exposed images reaches 8000 DNs in the panchromatic filter and thousands of DNs in the colour filters, the residuals are negligible and CaSSIS provides the best colour images available over many areas covered.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.187
Teacher spread0.181 · 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