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Record W4400415119 · doi:10.3847/1538-4357/ad487e

Chandra Study of the Proper Motion of HST-1 in the Jet of M87

2024· article· en· W4400415119 on OpenAlexaff
R. Thimmappa, Joey Neilsen, Daryl Haggard, Michael A. Nowak, Sera Markoff

Bibliographic record

VenueThe Astrophysical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsMcGill University
FundersNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsKnot (papermaking)BrightnessProper motionSuperluminal motionRadio galaxyFlux (metallurgy)Offset (computer science)Very Long Baseline ArrayJet (fluid)FlareObservableGalaxyAstronomyActive galactic nucleusStars

Abstract

fetched live from OpenAlex

Abstract The radio galaxy M87 is well known for its jet, which features a series of bright knots observable from radio to X-ray wavelengths. We analyze the X-ray image and flux variability of the knot HST-1 in the jet. Our analysis includes all 112 available Chandra ACIS-S observations from 2000 to 2021, with a total exposure time of ∼884 ks. We use deconvolved images to study the brightness profile of the X-ray jet and measure the relative separation between the core and HST-1. From 2003 to 2005 (which coincides with a bright flare from HST-1), we find a correlation between the flux of HST-1 and its offset from the core. In subsequent data, we find a steady increase in this offset, which implies a bulk superluminal motion for HST-1 of 6.6 ± 0.9 c (2.0 ± 0.3 pc yr−1), in keeping with prior results. We discuss models for the flux–offset correlation that feature either two or four emission regions separated by tens of parsecs. We attribute these results to moving shocks in the jet, which allow us to measure the internal structure of the jet.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 designObservational
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

Citations6
Published2024
Admission routes1
Has abstractyes

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