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A model for saturation correction in meteor photometry

2010· article· en· W1982730443 on OpenAlexaff
J. B. Kikwaya, R. Weryk, M. Campbell‐Brown, Peter Brown

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteoroidMeteor (satellite)Light curvePhotometry (optics)PhysicsSaturation (graph theory)OpticsAstrophysicsStarsAstronomyMathematics

Abstract

fetched live from OpenAlex

In order to correct for the effect of saturation on photometric measurements of meteors, we have developed a numerical model for saturation and apply it to data gathered using two generation III image intensified video systems on two nights (2008 October 31 and 2008 November 6). The two cameras were pointed in the same direction, and the aperture of one camera was set two stops below the aperture of the other. With these conditions, some meteors saturated one camera but not the other (group I); some saturated both cameras (group II); and some did not saturate either of them (group III). A model of meteor saturation has been developed which uses the image background value, angular meteor speed and the lateral width of the meteor image to simulate the true and saturated light curve of meteors. For group I meteors, we computed a saturation correction and applied it to the saturated light curve. We then compared the corrected saturated curve to the unsaturated curve from the other camera to validate the model. For group II meteors, a saturation correction is calculated and applied to both observed light curves, which have different degrees of saturation, and the corrected curves are compared. We collected 516 meteors, of which 30 were of group I, and seven of group II. For meteors in group I, an average residual of less than 0.4 mag was found between the observed unsaturated light curve and the model-corrected saturated light curve. For meteors in group II, the average residual between the two corrected light curves was 0.3 mag. For our data, the saturation correction goes from 0.5 to 1.9 mag for meteors in group I, and 1.2 to 2.5 mag for meteors in group II. Based on the agreement between the observed and modelled light curves (less than 0.4 mag over all meteors of all groups), we conclude that our model for saturation correction is valid. It can be used to extract the true luminosity of a saturated meteor, which is necessary to calculate photometric mass. Our model also demonstrates that fixed corrections to saturated meteor photometry, not accounting for background levels or angular velocities, do introduce significant error to meteor photometric analyses.

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)0.005
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: Methods · Consensus signal: Methods
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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
GenreMethods

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

Citations6
Published2010
Admission routes1
Has abstractyes

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