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Title: 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
NSF-PAR ID:
10398358
Author(s) / Creator(s):
; ; ; ; ;
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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