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Record W4280491392 · doi:10.21203/rs.3.rs-1631293/v1

Tomography of the Solar Corona with the Metis Coronagraph I: Predictive Simulations with Visible Light Images

2022· preprint· en· W4280491392 on OpenAlexaboutno aff
A. M. Vásquez, F. A. Nuevo, F. Frassati, А. Бемпорад, R. A. Frazin, M. Romoli, Nishtha Sachdeva, W. B. Manchester

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsnot available
FundersBundesministerium für Wirtschaft und EnergieNuclear Safety and Security CommissionAgencia Nacional de Promoción Científica y TecnológicaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsCoronagraphPhysicsSolar radiusExtreme ultravioletCorona (planetary geology)AstronomySolar minimumBrightnessAstrophysicsSolar windCoronal mass ejectionSolar cycleExoplanetOpticsPlasmaAstrobiologyPlanet

Abstract

fetched live from OpenAlex

Abstract The Metis coronagraph of the Solar Orbiter (SolO) mission records full-Sun images of the solar corona in Lyman-α ultraviolet (UV) radiation and in visible light polarized brightness (pB). This work investigates the utility of a synoptic observational program of Metis in terms of using its pB-images for tomographic reconstruction of the three-dimensional (3D) distribution of the electron density of the global solar corona. During its lifetime, SolO’s distance to the Sun will range D ≈ 0.3−1.0 au, while its solar latitude will span θ ≈ ±33 •. The limitations that such orbit complexity poses on tomographic reconstructions is explored in this work. Using SolO’s predictive orbital information and 3D MHD simulations of the solar corona, time series of synthetic Metis pB-images were computed and used as data to attempt tomographic reconstruction of the model. These numerical experiments were implemented for two Carrington rotations, corresponding to a solar minimum and a solar maximum, representative of extreme conditions of coronal complexity. For each rotation images were synthesized from three orbital segments, corresponding to extreme geometrical conditions of observation by Metis. For the early phase of the mission (year 2023), simulations were carried from the largest aphelion (D ≈ 0.95 au) and the smallest perihelion (D ≈ 0.29 au), both cases corresponding to low latitude (|θ| < 10 •) positions. For the late phase of the mission (year 2029), a simulation was carried out from the maximum solar latitude position (θ ≈ +33 •), at an intermediate distance (D ≈ 0.5 au). The range of heights that can be reconstructed and the required data-gathering time, both dependent on D, are reported for the six experiments. The extension of the coronal region that can be reconstructed and the accuracy of the reconstruction, both decreasing with increasing solar latitude |θ| as well as with increasing coronal complexity, are discussed in detail in each case. As a general conclusion, a Metis synoptic observational program with a cadence of at least 4 images/day provides enough data to attempt tomographic reconstructions of the coronal electron density during the whole lifetime of the mission, a requirement well within the 2 − 3 hr cadence of the current synoptic program. This program will allow implementation of tomography experimenting with different values for the cadence of the time-series of images used to feed reconstructions. Its cadence will also provide continuous opportunity to select images avoiding highly dynamic events, which compromise the accuracy of tomographic reconstructions.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.304
Teacher spread0.290 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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