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Record W4400290554 · doi:10.5194/epsc2024-626

Fresnel holography for radio characterization of meteoroid fragmentation

2024· preprint· en· W4400290554 on OpenAlexaffabout
Joachim Balis, Peter Brown, H. Lamy, Emmanuël Jehin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteoroidHolographyFragmentation (computing)Characterization (materials science)Fresnel zoneOpticsAstronomyPhysicsComputer science

Abstract

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Abstract: It has been argued within the scientific community that meteoroids of all sizes fragment. Observations with the high-resolution optical network CAMO (Canadian Automated Meteor Observatory) have shown obvious fragments for 90% of meteoroids (Subasinghe et al., 2016). The remaining 10% may fragment as well, as Campbell-Brown et al. (2017) showed that even meteors with a very short wake cannot be fitted with a single body model.Fragmentation is essential for a proper characterization of the structure and composition of meteoroids. In turn, the latter determine the dynamics of meteoroids in space, their ablation behavior when they enter the atmosphere and the potential damage that they cause to spacecraft. Ignoring fragmentation leads notably to an overestimation of ablation coefficients, an underestimation of meteoroid densities (Moorhead et al, 2017) and limit the accuracy in the determination of meteoroid orbits (Vida et al, 2018).Direct measurements of fragmentation are particularly important for a better understanding of the underlying phenomenon and for improving the existing ablation models. Although high-resolution observations of meteor trails through optical means at mm-sizes have been done in the recent years (e.g., Campbell-Brown, 2017; Vida et al., 2021), there are few works performing fragmentation characterization through radio observations.An approach that utilizes both the phase and amplitude associated to the radio echo of a meteor was developed by Elford (2001). This technique, called Fresnel Transform (FT), allows to study the structure of the ionized trail immediately behind the head of the meteor. Although it is highly effective for computing the variation of the electron line density along the trail and therefore characterizing fragmentation, to date the FT has been mainly used for the computation of meteoroid velocity (Baggaley & Grant, 2005; Campbell & Elford, 2006; Holdsworth et al., 2007; Roy et al., 2007).We will present progress of a new Python software package which allows the computation of the FT applied to meteor echoes, with a specific aim of statistically examining the process of fragmentation at small meteoroid sizes.With this software, we will apply the FT to meteor echoes detected by the Canadian Meteor Orbit Radar (CMOR) and compare the results with the high-resolution imagery furnished by CAMO as validation. This comparative analysis will validate both the accuracy of the Python tool and the physical interpretation of the scattering amplitudes produced by the FT.This tool will be applicable in an automated mode to both backscatter and forward-scatter data, providing a versatile framework for meteoroid analysis. This project will, for the first time, enable us to examine fragmentation of small meteoroids in a self-contained manner. References: - Subasinghe D., Campbell-Brown M. D., Stokan E. (2016). Physical characteristics of faint meteors by light curve and high-resolution observations, and the implications for parent bodies. Monthly Notices of the Royal Astronomical Society, 457(2), 1289–1298. https://doi.org/10.1093/mnras/stw019- Campbell-Brown M. D. (2017). Modelling a short-wake meteor as a single or fragmenting body. Planetary and Space Science, 143, 34-39. https://doi.org/10.1016/j.pss.2017.02.012- Moorhead A. V., Blaauw R. C., Moser D. E, Campbell-Brown M. D., Brown. P.G., Cooke W. J. (2017). A two-population sporadic meteoroid bulk density distribution and its implications for environment models. Monthly Notices of the Royal Astronomical Society, 472(4), 3833-3841. https://doi.org/10.1093/mnras/stx2175- Vida D., Brown P. G., Campbell-Brown M. D. (2018). Modelling the measurement accuracy of pre-atmosphere velocities of meteoroids. Monthly Notices of the Royal Astronomical Society, 479(4), 4307–4319. https://doi.org/10.1093/mnras/sty1841- Vida D., Brown P. G., Campbell-Brown M. D, Weryk R. J., Stober G., McCormack J. P. (2021). High precision meteor observations with the Canadian automated meteor observatory: Data reduction pipeline and application to meteoroid mechanical strength measurements. Icarus, 354, 114097. https://doi.org/10.1016/j.icarus.2020.114097.- Elford W. G. (2001). Observations of the structure of meteor trails at radio wavelengths using Fresnel holography. ESASP, 495, 405–411. https://ui.adsabs.harvard.edu/abs/2001ESASP.495..405E/abstract- Baggaley W. J., Grant J. (2005). Techniques for Measuring Radar Meteor Speeds. In: Hawkes, R., Mann, I., Brown, P. (eds). Modern meteor science: an interdisciplinary view. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5075-5_56- Campbell L. A., Elford W. G. (2006). Accuracy of meteoroid speeds determined using a Fresnel transform procedure. Planetary and Space Science, 54(3), 317–323. https://doi.org/10.1016/j.pss.2005.12.016- Holdsworth D. A., Elford W. G., Vincent R. A., Reid I. M., Murphy D. J., Singer W. (2007). All-sky interferometric meteor radar meteoroid speed estimation using the Fresnel transform. Annales Geophysicae, 25(2), 385–398. https://doi.org/10.5194/angeo-25-385-2007- Roy A., Doherty J. F., Mathews J. D. (2007). Analyzing radar meteor trail echoes using the Fresnel transform technique: a signal processing viewpoint. Earth Moon Planet, 101, 27–39. https://doi.org/10.1007/s11038-007-9147-5

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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