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Abstract Models of the resolved Event Horizon Telescope (EHT) sources Sgr A* and M87* are constrained by observations at multiple wavelengths, resolutions, polarizations, and time cadences. In this paper, we compare unresolved circular polarization (CP) measurements to a library of models, where each model is characterized by a distribution of CP over time. In the library, we vary the spin of the black hole, the magnetic field strength at the horizon (i.e., both SANE and magnetically arrested disk or MAD models), the observer inclination, a parameter for the maximum ion–electron temperature ratio assuming a thermal plasma, and the direction of the magnetic field dipole moment. We find that Atacama Large Millimeter/submillimeter Array (ALMA) observations of Sgr A* are inconsistent with all edge-on (i= 90°) models. Restricting attention to the MAD models favored by earlier EHT studies of Sgr A*, we find that only models with magnetic dipole moment pointing away from the observer are consistent with ALMA data. We also note that in 26 of the 27 passing MAD models, the accretion flow rotates clockwise on the sky. We provide a table of the means and standard deviations of the CP distributions for all model parameters, along with their trends.
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Using Machine Learning to link black hole accretion flows with spatially resolved polarimetric observables
ABSTRACT We introduce a new library of 535 194 model images of the supermassive black holes and Event Horizon Telescope (EHT) targets Sgr A* and M87*, computed by performing general relativistic radiative transfer calculations on general relativistic magnetohydrodynamics simulations. Then to infer underlying black hole and accretion flow parameters (spin, inclination, ion-to-electron temperature ratio, and magnetic field polarity), we train a random forest machine learning model on various hand-picked polarimetric observables computed from each image. Our random forest is capable of making meaningful predictions of spin, inclination, and the ion-to-electron temperature ratio, but has more difficulty inferring magnetic field polarity. To disentangle how physical parameters are encoded in different observables, we apply two different metrics to rank the importance of each observable at inferring each physical parameter. Details of the spatially resolved linear polarization morphology stand out as important discriminators between models. Bearing in mind the theoretical limitations and incompleteness of our image library, for the real M87* data, our machinery favours high-spin retrograde models with large ion-to-electron temperature ratios. Due to the time-variable nature of these targets, repeated polarimetric imaging will further improve model inference as the EHT and next-generation (EHT) continue to develop and monitor their targets.
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- Award ID(s):
- 1743747
- PAR ID:
- 10398358
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 520
- Issue:
- 4
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- p. 4867-4888
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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