Psaltis, Dimitrios and Özel, Feryal and Medeiros, Lia and Christian, Pierre and Kim, Junhan and Chan, Chi-kwan and Conway, Landen J. and Raithel, Carolyn A. and Marrone, Dan and Lauer, Tod R. (2022) Markov Chains for Horizons MARCH. I. Identifying Biases in Fitting Theoretical Models to Event Horizon Telescope Observations. Astrophysical Journal, 928 (1). Art. No. 55. ISSN 0004-637X. doi:10.3847/1538-4357/ac2c69. https://resolver.caltech.edu/CaltechAUTHORS:20220325-519017618
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Abstract
We introduce a new Markov Chain Monte Carlo (MCMC) algorithm with parallel tempering for fitting theoretical models of horizon-scale images of black holes to the interferometric data from the Event Horizon Telescope (EHT). The algorithm implements forms of the noise distribution in the data that are accurate for all signal-to-noise ratios. In addition to being trivially parallelizable, the algorithm is optimized for high performance, achieving 1 million MCMC chain steps in under 20 s on a single processor. We use synthetic data for the 2017 EHT coverage of M87 that are generated based on analytic as well as General Relativistic Magnetohydrodynamic (GRMHD) model images to explore several potential sources of biases in fitting models to sparse interferometric data. We demonstrate that a very small number of data points that lie near salient features of the interferometric data exert disproportionate influence on the inferred model parameters. We also show that the preferred orientations of the EHT baselines introduce significant biases in the inference of the orientation of the model images. Finally, we discuss strategies that help identify the presence and severity of such biases in realistic applications.
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| Additional Information: | © 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2020 May 19; revised 2021 September 3; accepted 2021 October 1; published 2022 March 25. We thank K. Satapathy and T. Trent for useful discussions. This work was supported in part by NSF PIRE grant 1743747 and NSF grant AST 1715061. L.M. acknowledges support from an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award no. AST-1903847. C.R. acknowledges support from NSF Graduate Research Fellowship Program Grant DGE-1746060. All ray-tracing calculations were performed with the El Gato GPU cluster at the University of Arizona that is funded by NSF award 1228509. | ||||||||||||||||||||
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| Subject Keywords: | Supermassive black holes; Astrophysical black holes; Astrostatistics; Algorithms; Very long baseline interferometry; Interferometry | ||||||||||||||||||||
| Issue or Number: | 1 | ||||||||||||||||||||
| Classification Code: | Unified Astronomy Thesaurus concepts: Supermassive black holes (1663); Astrophysical black holes (98); Astrostatistics (1882); Algorithms (1883); Very long baseline interferometry (1769); Interferometry (808) | ||||||||||||||||||||
| DOI: | 10.3847/1538-4357/ac2c69 | ||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20220325-519017618 | ||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20220325-519017618 | ||||||||||||||||||||
| Official Citation: | Dimitrios Psaltis et al 2022 ApJ 928 55 | ||||||||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||||||
| ID Code: | 114076 | ||||||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||||||
| Deposited By: | George Porter | ||||||||||||||||||||
| Deposited On: | 25 Mar 2022 11:33 | ||||||||||||||||||||
| Last Modified: | 25 Mar 2022 21:36 |
Available Versions of this Item
- Markov Chains for Horizons MARCH. I. Identifying Biases in Fitting Theoretical Models to Event Horizon Telescope Observations. (deposited 25 Mar 2022 11:33) [Currently Displayed]
Commentary/Response Threads
- Psaltis, Dimitrios and Özel, Feryal and Medeiros, Lia and Christian, Pierre and Kim, Junhan and Chan, Chi-kwan and Conway, Landen J. and Raithel, Carolyn A. and Marrone, Dan and Lauer, Tod R. Markov Chains for Horizons MARCH. I. Identifying Biases in Fitting Theoretical Models to Event Horizon Telescope Observations. (deposited 25 Mar 2022 11:33) [Currently Displayed]
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