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Variable structures in M87(star) from space, time and frequency resolved interferometry

MPS-Authors
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Arras,  Philipp
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Frank,  Philipp
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Haim,  Philipp
MPI for Astrophysics, Max Planck Society;

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Knollmüller,  Jakob
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Leike,  Reimar
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

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Reinecke,  Martin
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

/persons/resource/persons16142

Enßlin,  Torsten
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Citation

Arras, P., Frank, P., Haim, P., Knollmüller, J., Leike, R., Reinecke, M., et al. (2022). Variable structures in M87(star) from space, time and frequency resolved interferometry. Nature astronomy, 2022. doi:10.1038/s41550-021-01548-0.


Cite as: http://hdl.handle.net/21.11116/0000-0009-CEF8-8
Abstract
The immediate vicinity of an active supermassive black hole—with its event horizon, photon ring, accretion disk and relativistic jets—is an appropriate place to study physics under extreme conditions, particularly general relativity and magnetohydrodynamics. Observing the dynamics of such compact astrophysical objects provides insights into their inner workings, and the recent observations of M87* by the Event Horizon Telescope1,2,3,4,5,6 using very-long-baseline interferometry techniques allows us to investigate the dynamical processes of M87* on timescales of days. Compared with most radio interferometers, very-long-baseline interferometry networks typically have fewer antennas and low signal-to-noise ratios. Furthermore, the source is variable, prohibiting integration over time to improve signal-to-noise ratio. Here, we present an imaging algorithm7,8 that copes with the data scarcity and temporal evolution, while providing an uncertainty quantification. Our algorithm views the imaging task as a Bayesian inference problem of a time-varying brightness, exploits the correlation structure in time and reconstructs (2 + 1 + 1)-dimensional time-variable and spectrally resolved images. We apply this method to the Event Horizon Telescope observations of M87*9 and validate our approach on synthetic data. The time- and frequency-resolved reconstruction of M87* confirms variable structures on the emission ring and indicates extended and time-variable emission structures outside the ring itself.