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Record W4409820307 · doi:10.1051/0004-6361/202452837

Quantifying jet–interstellar medium interactions in Cyg X-1: Insights from dual-frequency bow shock detection with MeerKAT

2025· article· en· W4409820307 on OpenAlexfundno aff
P. Atri, S. Motta, J. van den Eijnden, James Matthews, J. C. A. Miller‐Jones, R. P. Fender, D. R. Williams, Ian Heywood, P. A. Woudt

Bibliographic record

VenueAstronomy and Astrophysics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
FundersASTRONUniversity of Cape TownInstitut sur la Nutrition et les Aliments FonctionnelsUniversiteit StellenboschHORIZON EUROPE Framework ProgrammeNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of the Western CapeRoyal Astronomical SocietyUniversity of PretoriaUniversity of WarwickEuropean CommissionCape Peninsula University of Technology
KeywordsPhysicsAstrophysicsInterstellar mediumBow shock (aerodynamics)Jet (fluid)Shock (circulatory)Shock waveAstronomyDual (grammatical number)GalaxyMechanicsMedicine

Abstract

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Context. Accretion and outflows are astrophysical phenomena observed across a wide range of objects, from white dwarfs to supermassive black holes. Developing a complete picture of these processes requires complementary studies across this full spectrum of jet-launching sources. Jet–interstellar medium (ISM) interaction sites near black hole X-ray binaries provide unique laboratories that provide insights into the energetics of the jets launched from stellar-mass black holes. Aims. This work aims to detect and characterise the bow shock near one black hole X-ray binary, Cyg X-1, and then use this bow shock structure to parametrise the properties of the jet launched by Cyg X-1 over its lifetime. Methods. We used the MeerKAT radio telescope to investigate the bow shock structure formed by the interaction between the jets of Cyg X-1 and the ISM. Using new L - and S -band detections of the bow shock, we constrained the density of the unshocked ISM and mapped the bow shock’s spectral index. These values were applied to self-similar models developed initially for FR II galaxies to estimate the energy transport rate and the age of Cyg X-1 jets. Results. We successfully detect the bow shock north of Cyg X-1 in the L and S bands and report its size and brightness. We present the spectral index distribution across the bow shock, which is in the range −0.9 ≤ α ≤ 0.4, with an error distribution (0.6 ≤ Δ α ≤ 1.5) that peaks at unity. We determine that the unshocked ISM density is 6–7 cm −3 for a temperature range of 10 4 –3 × 10 6 K. This temperature range suggests that the velocity of the bow shock is 21 km s −1 < L̇ < 364 km s −1 . The age of the Cyg X-1 jet responsible for the bow shock is 0.04–0.3 Myr, and the power of the jet is constrained to 2 × 10 31 ergs s −1 < Q jet a < 1 × 10 35 ergs s −1 for the case of opening angles of 0.3°–2.0°. We also detect new morphological features of the bow shock in the S -band image. The comparison of archival H α maps with the new radio observations hints at different regions of emission, different temperature ranges, and different ISM densities. Conclusions. MeerKAT’s sensitivity and resolution effectively reveal low surface brightness features of the Cyg X-1 bow shock. The spectral index suggests a consistent emission origin across the structure. The ISM density around Cyg X-1 is on the higher end for Galactic environments, and our results indicate a lower jet energy transport rate than prior estimates. Further searches with MeerKAT will help build a statistically significant sample, advancing our understanding of black hole X-ray binary jets and their impact on their local environments.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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
Published2025
Admission routes1
Has abstractyes

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