2020A&A...636A..94V


Query : 2020A&A...636A..94V

2020A&A...636A..94V - Astronomy and Astrophysics, volume 636A, 94-94 (2020/4-1)

Deep Horizon: A machine learning network that recovers accreting black hole parameters.

VAN DER GUCHT J., DAVELAAR J., HENDRIKS L., PORTH O., OLIVARES H., MIZUNO Y., FROMM C.M. and FALCKE H.

Abstract (from CDS):


Context. The Event Horizon Telescope recently observed the first shadow of a black hole. Images like this can potentially be used to test or constrain theories of gravity and deepen the understanding in plasma physics at event horizon scales, which requires accurate parameter estimations.
Aims. In this work, we present Deep Horizon, two convolutional deep neural networks that recover the physical parameters from images of black hole shadows. We investigate the effects of a limited telescope resolution and observations at higher frequencies.
Methods. We trained two convolutional deep neural networks on a large image library of simulated mock data. The first network is a Bayesian deep neural regression network and is used to recover the viewing angle i, and position angle, mass accretion rate M, electron heating prescription Rhigh and the black hole mass MBH. The second network is a classification network that recovers the black hole spin a.
Results. We find that with the current resolution of the Event Horizon Telescope, it is only possible to accurately recover a limited number of parameters of a static image, namely the mass and mass accretion rate. Since potential future space-based observing missions will operate at frequencies above 230GHz, we also investigated the applicability of our network at a frequency of 690GHz. The expected resolution of space-based missions is higher than the current resolution of the Event Horizon Telescope, and we show that Deep Horizon can accurately recover the parameters of simulated observations with a comparable resolution to such missions.

Abstract Copyright: © ESO 2020

Journal keyword(s): accretion, accretion disks - black hole physics - radiative transfer - methods: data analysis

Simbad objects: 2

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Number of rows : 2
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2022
#notes
1 M 87 BiC 12 30 49.42338230 +12 23 28.0438581 10.16 9.59 8.63   7.49 ~ 6647 3
2 NAME Sgr A* X 17 45 40.03599 -29 00 28.1699           ~ 3866 3

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2022.05.24-11:02:45

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