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Record W3132195051 · doi:10.1007/s11214-021-00795-x

Pre-Flight Calibration of the Mars 2020 Rover Mastcam Zoom (Mastcam-Z) Multispectral, Stereoscopic Imager

2021· review· en· W3132195051 on OpenAlexaff
Alexander G. Hayes, P. Corlies, Christian Tate, M. Barrington, J. F. Bell, J. N. Maki, M. A. Caplinger, M. A. Ravine, K. M. Kinch, K. E. Herkenhoff, B. Horgan, J. R. Johnson, M. T. Lemmon, Gerhard Paar, M. S. Rice, Elsa Jensen, Tex Kubacki, E. A. Cloutis, R. Deen, B. L. Ehlmann, Emily Lakdawalla, R. Sullivan, A. Winhold, Alexis Parkinson, Zachary Bailey, Jason Van Beek, Piluca Caballo-Perucha, E. Cisneros, Darian T. Dixon, Christopher Donaldson, Ole Bjarlin Jensen, J. C. Kuik, Kristiana Lapo, Angela Magee, Marco Merusi, J. Mollerup, N. A. Scudder, Christina H. Seeger, Evan Stanish, M. Starr, Mark Thompson, Nathalie Turenne, K. Winchell

Bibliographic record

VenueSpace Science Reviews · 2021
Typereview
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Winnipeg
FundersÖsterreichische ForschungsförderungsgesellschaftJet Propulsion LaboratoryAmes Research CenterU.S. Geological SurveyNational Aeronautics and Space Administration
KeywordsMars Exploration ProgramMultispectral imageRemote sensingStereoscopyCalibrationZoomPlanetary scienceGeologyMars roverAstrobiologyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The NASA Perseverance rover Mast Camera Zoom (Mastcam-Z) system is a pair of zoomable, focusable, multi-spectral, and color charge-coupled device (CCD) cameras mounted on top of a 1.7 m Remote Sensing Mast, along with associated electronics and two calibration targets. The cameras contain identical optical assemblies that can range in focal length from 26 mm ( $25.5^{\circ }\, \times 19.1^{\circ }\ \mathrm{FOV}$ 25.5 ∘ × 19.1 ∘ FOV ) to 110 mm ( $6.2^{\circ } \, \times 4.2^{\circ }\ \mathrm{FOV}$ 6.2 ∘ × 4.2 ∘ FOV ) and will acquire data at pixel scales of 148-540 μm at a range of 2 m and 7.4-27 cm at 1 km. The cameras are mounted on the rover’s mast with a stereo baseline of $24.3\pm 0.1$ 24.3 ± 0.1 cm and a toe-in angle of $1.17\pm 0.03^{\circ }$ 1.17 ± 0.03 ∘ (per camera). Each camera uses a Kodak KAI-2020 CCD with $1600\times 1200$ 1600 × 1200 active pixels and an 8 position filter wheel that contains an IR-cutoff filter for color imaging through the detectors’ Bayer-pattern filters, a neutral density (ND) solar filter for imaging the sun, and 6 narrow-band geology filters (16 total filters). An associated Digital Electronics Assembly provides command data interfaces to the rover, 11-to-8 bit companding, and JPEG compression capabilities. Herein, we describe pre-flight calibration of the Mastcam-Z instrument and characterize its radiometric and geometric behavior. Between April 26 $^{th}$ t h and May 9 $^{th}$ t h , 2019, ∼45,000 images were acquired during stand-alone calibration at Malin Space Science Systems (MSSS) in San Diego, CA. Additional data were acquired during Assembly Test and Launch Operations (ATLO) at the Jet Propulsion Laboratory and Kennedy Space Center. Results of the radiometric calibration validate a 5% absolute radiometric accuracy when using camera state parameters investigated during testing. When observing using camera state parameters not interrogated during calibration (e.g., non-canonical zoom positions), we conservatively estimate the absolute uncertainty to be $<10\%$ < 10 % . Image quality, measured via the amplitude of the Modulation Transfer Function (MTF) at Nyquist sampling (0.35 line pairs per pixel), shows $\mathrm{MTF}_{\mathit{Nyquist}}=0.26-0.50$ MTF Nyquist = 0.26 − 0.50 across all zoom, focus, and filter positions, exceeding the $>0.2$ > 0.2 design requirement. We discuss lessons learned from calibration and suggest tactical strategies that will optimize the quality of science data acquired during operation at Mars. While most results matched expectations, some surprises were discovered, such as a strong wavelength and temperature dependence on the radiometric coefficients and a scene-dependent dynamic component to the zero-exposure bias frames. Calibration results and derived accuracies were validated using a Geoboard target consisting of well-characterized geologic samples.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.005

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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations56
Published2021
Admission routes1
Has abstractyes

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