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Record W3096640497 · doi:10.5194/epsc2020-64

Classification of fireballs: upgrading the PE criterion

2020· article· en· W3096640497 on OpenAlexaff
Manuel Moreno-Ibáñez, Maria Gritsevich, J. M. Trigo‐Rodríguez, Elizabeth A. Silber, Jouni Peltoniemi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteor (satellite)MeteoroidValue (mathematics)Interpretation (philosophy)Computer scienceArtificial intelligenceOperations researchMeteorologyStatisticsMathematicsPhysicsMachine learningAstronomy

Abstract

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The visual observation of meteors has gathered over the last century the interest of scientists and the fascination of public. As the meteor observational techniques were spread worldwide, the meteor research community was avid of reliable mathematical relationships to derive further clues on these events: their orbital origin, their hazardous potential, etc. For such purpose, the combination of the meteor event observed flight parameters seemed to comply with these goals. Moreover, this approach was little by little more feasible given the technology growth that gradually improved the accuracy of the observations. Amongst all the suggested flight parameter relationships available in the literature, the one introduced by Ceplecha and McCrosky [1] in the mid 70’s became timely and used in many studies as a ‘ground truth’. The empirical work of Ceplecha and McCrosky [1] was also mathematically supported by the single body Newtonian formulation that is still widely used to model the meteor trajectory [2]. Moreover, Ceplecha and McCrosky [1] expanded the use of this relationship to elaborate a meteor classification. The classification relies on the value of a criterion, called PE, which ranks the value of the correlation given by the parameters included in the relationship. Although the authors did not expect the classification to be extremely accurate, it allows quick interpretation of the event under study. Consequently, both the criterion and the classification have played a relevant role in the scientific publication over the decades.The PE criterion relates the meteor observed end height to its atmospheric entry velocity, mass and flight trajectory angle. To keep the PE calculation straightforward, and because it originally was used for decelerating events, the influence of the meteoroid mass loss (ablation and shape coefficient) was simplified and a mean value for all the meteors registered in the same database was assumed. In the recent years, several alternative formulations for the meteoroid atmospheric flight modelling have been proposed in order to reduce the required analysis-related assumptions and consequent results’ inaccuracies. Amongst them, a formulation based on scaling laws and dimensionless variables has obtained significant results when tackling with different common meteor related studies (see e.g. [3-9]). The main advantage of this methodology is that it provides relevant clues on the event under study removing the necessity of stating initial assumptions on the meteor parameters. Additionally, the accuracy of the outcome is, in most cases, directly linked to the quality of the observations. Interestingly, this new methodology quantifies the meteoroid mass loss in a unique and straightforward way by matching the meteor trajectory (observed height and velocity values) with two dimensionless parameters that are physically meaningful. On one hand, the ballistic coefficient, α, expresses the drag intensity suffered by the meteor body during its flight and it is proportional to the mass of the atmospheric column with the initial meteoroid cross section area along the trajectory divided by the meteoroid’s pre-atmospheric mass. On the other, the mass loss parameter, β, characterizes the mass loss rate of the meteoroid; it can be expressed as the fraction of the kinetic energy per mass unit of the body that is transferred to the body in the form of heat divided by the effective destruction enthalpy.Since these two parameters comprise all the meteoroid flight variables earlier included in the PE criterion, but avoid artificial assumptions, we have proposed and studied the hypothesis that there should exist a mathematical expression involving these two parameters which offers an improved classification criterion [2]. In this work, we verify this hypothesis. The results of our study show that: i) under the same original assumptions [1] the derived log(2αβ) which we advocate using leads to the exactly same PE formula obtained by Ceplecha and McCrosky [1]; ii) the newly offered possibility to include the individual event mass-loss effects in the criterion allows an accurate formulation that still remains simple to implement; iii) the improved criterion is scalable – it is suitable for expanding the classification beyond fully disintegrating fireballs to larger impactors, including meteorite-dropping fireballs. We use the Prairie Network meteor observations for comparative analysis, which demonstrates the effectiveness and reliability of the new formulation.References[1] Ceplecha Z., McCrosky R. E.,1976, JGR, 81, 6257. https://doi.org/10.1029/JB081i035p06257[2] Moreno-Ibáñez M., Gritsevich M., Trigo-Rodriguez J. M., Silber E. A., 2020, MNRAS, 494 (1), 316. https://doi.org/10.1093/mnras/staa646[3] Gritsevich M. I., 2009, Adv. Space Res., 44(3), 323. http://dx.doi.org/10.1016/j.asr.2009.03.030[4] Gritsevich M. I., Stulov V. P., Turchak L. I.,2012, Cosmic Res., 50(1), 56. http://dx.doi.org/10.1134/S0010952512010017[5] Bouquet A., Baratoux D., Vaubaillon J., et al. 2014, PSS, 103, 238. http://dx.doi.org/10.1016/j.pss.2014.09.001[6] Moreno-Ibáñez M., Gritsevich M., Trigo-Rodríguez J. M., 2015, Icarus, 250, 544. http://dx.doi.org/10.1016/j.icarus.2014.12.027[7] Trigo-Rodríguez J. M., Lyytinen E., Gritsevich M., et al., 2015, MNRAS,449 (2), 2119. http://dx.doi.org/10.1093/mnras/stv378[8] Lyytinen E., Gritsevich M., 2016, PSS, 120, 35. http://dx.doi.org/10.1016/j.pss.2015.10.012[9] Sansom E. K., Gritsevich M., Devillepoix H. A. R., et al., 2019, ApJ, 885 (2). https://doi.org/10.3847/1538-4357/ab4516

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)0.005
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.031
GPT teacher head0.238
Teacher spread0.207 · 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 designTheoretical or conceptual
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".

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Citations0
Published2020
Admission routes1
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