Jet kinematics in the transversely stratified jet of 3C 84: A two-decade overview

Creators: Paraschos, G. F.¹; Krichbaum, T. P.¹; Kim, J.-Y.^{2, 3, 1}; Hodgson, J. A.^{4, 3}; Oh, J.⁴; Ros, E.¹; Zensus, J. A.¹; Marscher, A. P.⁵; Jorstad, S. G.^{5, 6}; Gurwell, M. A.⁷; Lähteenmäki, A.⁸; Tornikoski, M.⁸; Kiehlmann, S.⁹; Readhead, A. C. S.¹⁰

1. Max Planck Institute for Radio Astronomy
2. Kyungpook National University
3. Korea Astronomy and Space Science Institute
4. Sejong University
5. Boston University
6. St Petersburg University
7. Harvard-Smithsonian Center for Astrophysics
8. Aalto University
9. University of Crete
10. California Institute of Technology

Abstract

3C 84 (NGC 1275) is one of the brightest radio sources in the millimetre radio bands, which led to a plethora of very-long-baseline interferometry (VLBI) observations at numerous frequencies over the years. They reveal a two-sided jet structure, with an expanding but not well-collimated parsec-scale jet, pointing southward. High-resolution millimetre-VLBI observations allow the study and imaging of the jet base on a sub-parsec scale. This could facilitate the investigation of the nature of the jet origin, also in view of the previously detected two-railed jet structure and east-west oriented core region seen withRadioAstronat 22 GHz. We produced VLBI images of this core and inner jet region, observed over the past twenty years at 15, 43, and 86 GHz. We determined the kinematics of the inner jet and ejected features at 43 and 86 GHz and compared their ejection times with radio andγ-ray variability. For the moving jet features, we find an average velocity of β_(app)^(avg) = 0.055−0.22c (μ^(avg) = 0.04 − 0.18 mas yr⁻¹). From the time-averaged VLBI images at the three frequencies, we measured the transverse jet width along the bulk flow. On the ≤1.5 parsec scale, we find a clear trend of the jet width being frequency dependent, with the jet being narrower at higher frequencies. This stratification is discussed in the context of a spine-sheath scenario, and we compare it to other possible interpretations. From quasi-simultaneous observations at 43 and 86 GHz, we obtain spectral index maps, revealing a time-variable orientation of the spectral index gradient due to structural variability of the inner jet.

Copyright and License

Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Open access funding provided by Max Planck Society.

Acknowledgement

We thank T. Savolainen for providing software to calculate two dimensional cross-correlations. We also thank N. R. MacDonald for the proofreading and fruitful discussions which helped improve this manuscript. We thank the anonymous referee for the useful comments. G.F. P. is supported for this research by the International Max-Planck Research School (IMPRS) for Astronomy and Astrophysics at the University of Bonn and Cologne. J.-Y. K. acknowledges support from the National Research Foundation (NRF) of Korea (grant no. 2022R1C1C1005255). This research has made use of data obtained with the Global Millimeter VLBI Array (GMVA), which consists of telescopes operated by the MPIfR, IRAM, Onsala, Metsähovi, Yebes, the Korean VLBI Network, the Green Bank Observatory and the Very Long Baseline Array (VLBA). The VLBA and the GBT are a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The data were correlated at the correlator of the MPIfR in Bonn, Germany. This work makes use of 37 GHz, and 230 and 345 GHz light curves kindly provided by the Metsähovi Radio Observatory and the Submillimeter Array (SMA), respectively. The SMA is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. This research has made use of data from the University of Michigan Radio Astronomy Observatory which has been supported by the University of Michigan and by a series of grants from the National Science Foundation, most recently AST-0607523. This work makes use of the Swinburne University of Technology software correlator, developed as part of the Australian Major National Research Facilities Programme and operated under licence. This study makes use of 43 GHz VLBA data from the VLBA-BU Blazar Monitoring Program (VLBA-BU-BLAZAR; http://www.bu.edu/blazars/VLBAproject.html), funded by NASA through the Fermi Guest Investigator Program. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team (Lister et al. 2009b). This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has also made use of NASA’s Astrophysics Data System Bibliographic Services. This research has also made use of data from the OVRO 40-m monitoring program (Richards et al. 2011), supported by private funding from the California Institute of Technology and the Max Planck Institute for Radio Astronomy, and by NASA grants NNX08AW31G, NNX11A043G, and NNX14AQ89G and NSF grants AST-0808050 and AST-1109911. S.K. acknowledges support from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme under grant agreement No. 771282. Finally, this research made use of the following python packages: numpy (Harris et al. 2020), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), astropy (Astropy Collaboration 2013, 2018), pandas (Pandas Development Team 2020; McKinney et al. 2010), seaborn (Waskom 2021), and Uncertainties: a Python package for calculations with uncertainties.

Software References

This research made use of the following python packages: numpy (Harris et al. 2020), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), astropy (Astropy Collaboration 2013, 2018), pandas (Pandas Development Team 2020; McKinney et al. 2010), seaborn (Waskom 2021), and Uncertainties: a Python package for calculations with uncertainties.

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