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Record W4379054741 · doi:10.3847/1538-4357/acb93a

Modeling the 2022 τ-Herculid Outburst

2023· article· en· W4379054741 on OpenAlexafffundabout
Auriane Egal, Paul Wiegert, Peter Brown, Denis Vida

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsRio Tinto (Canada)Espace pour la vieWestern University
FundersNuclear Safety and Security CommissionNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Aeronautics and Space Administration
KeywordsPhysicsAstronomyAstrophysicsAstrobiology

Abstract

fetched live from OpenAlex

Abstract The τ -Herculids (IAU shower number 61 TAH) is a minor meteor shower associated with comet 73P/Schwassmann–Wachmann 3, a Jupiter-family comet that disintegrated into several fragments in 1995. As a consequence of the nucleus breakup, possible increased meteor rates were predicted for 2022. On May 30–31, observation networks around the world reported two distinct peaks of TAH activity, around solar longitudes 69.°02 and 69.°42. This work examines the encounter conditions of the Earth with meteoroids ejected from 73P during the splitting event and on previous perihelion passages. Numerical simulations suggest that the main peak observed in 2022 was caused by meteoroids ejected from the splitting nucleus with four times the typical cometary gas expansion speed. High-resolution measurements performed with the Canadian Automated Meteor Observatory indicate that these meteoroids are fragile, with estimated bulk densities of 250 kg m −3 . In contrast with the main peak, the first TAH activity peak in 2022 is best modeled with trails ejected prior to 1960. We find that ordinary cometary activity could have produced other TAH apparitions observed in the past, including in 1930 and 2017. The extension of our model to future years predicts significant returns of the shower in 2033 and 2049.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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 teacher head, not a consensus.

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".

Quick stats

Citations10
Published2023
Admission routes3
Has abstractyes

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