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

van der Gucht, Jeffrey; Davelaar, Jordy; Hendriks, Luc; Porth, Oliver; Olivares, Hector; Mizuno, Yosuke; Fromm, Christian M.; Falcke, Heino

doi:10.1051/0004-6361/201937014

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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., et al. (2020). Deep Horizon: A machine learning network that recovers accreting black hole parameters. åp, 636: A94, pp. A94. doi:10.1051/0004-6361/201937014.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0008-43B1-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0008-43B2-3

Genre: Journal Article

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Creators:
van der Gucht, Jeffrey, Author
Davelaar, Jordy, Author
Hendriks, Luc, Author
Porth, Oliver, Author
Olivares, Hector, Author
Mizuno, Yosuke, Author
Fromm, Christian M.¹, Author
Falcke, Heino, Author

Affiliations:
1Max Planck Institute for Radio Astronomy, Max Planck Society, ou_2205652

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Free keywords: accretion, accretion disks, black hole physics, radiative transfer, methods: data analysis, Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology

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Dates: Date issued: 2020-04

Publication Status: Issued

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Identifiers: DOI: 10.1051/0004-6361/201937014
BibTex Citekey: 2020A&A...636A..94V

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Title: åp

Source Genre: Journal

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Pages: - Volume / Issue: 636 Sequence Number: A94 Start / End Page: A94 Identifier: -