Deep learning inference with the Event Horizon Telescope I. Calibration improvements and a comprehensive synthetic data library
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908409204310016 |
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| author | Janssen, M. Chan, C. -k. Davelaar, J. Natarajan, I. Olivares, H. Ripperda, B. Röder, J. Rynge, M. Wielgus, M. |
| author_facet | Janssen, M. Chan, C. -k. Davelaar, J. Natarajan, I. Olivares, H. Ripperda, B. Röder, J. Rynge, M. Wielgus, M. |
| contents | (abridged) In a series of publications, we describe a comprehensive comparison of Event Horizon Telescope (EHT) data with theoretical models of Sgr A* and M87*. Here, we report on improvements made to our observational data reduction pipeline and present the generation of observables derived from the EHT models. We make use of ray-traced GRMHD simulations that are based on different black hole spacetime metrics and accretion physics parameters. These broad classes of models provide a good representation of the primary targets observed by the EHT. To generate realistic synthetic data from our models, we took the signal path as well as the calibration process, and thereby the aforementioned improvements, into account. We could thus produce synthetic visibilities akin to calibrated EHT data and identify salient features for the discrimination of model parameters. We have produced a library consisting of an unparalleled 962,000 synthetic Sgr A* and M87* datasets. In terms of baseline coverage and noise properties, the library encompasses 2017 EHT measurements as well as future observations with an extended telescope array. We differentiate between robust visibility data products related to model features and data products that are strongly affected by data corruption effects. Parameter inference is mostly limited by intrinsic model variability, which highlights the importance of long-term monitoring observations with the EHT. In later papers in this series, we will show how a Bayesian neural network trained on our synthetic data is capable of dealing with the model variability and extracting physical parameters from EHT observations. With our calibration improvements, our newly reduced EHT datasets have a considerably better quality compared to previously analyzed data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning inference with the Event Horizon Telescope I. Calibration improvements and a comprehensive synthetic data library Janssen, M. Chan, C. -k. Davelaar, J. Natarajan, I. Olivares, H. Ripperda, B. Röder, J. Rynge, M. Wielgus, M. Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Computational Physics (abridged) In a series of publications, we describe a comprehensive comparison of Event Horizon Telescope (EHT) data with theoretical models of Sgr A* and M87*. Here, we report on improvements made to our observational data reduction pipeline and present the generation of observables derived from the EHT models. We make use of ray-traced GRMHD simulations that are based on different black hole spacetime metrics and accretion physics parameters. These broad classes of models provide a good representation of the primary targets observed by the EHT. To generate realistic synthetic data from our models, we took the signal path as well as the calibration process, and thereby the aforementioned improvements, into account. We could thus produce synthetic visibilities akin to calibrated EHT data and identify salient features for the discrimination of model parameters. We have produced a library consisting of an unparalleled 962,000 synthetic Sgr A* and M87* datasets. In terms of baseline coverage and noise properties, the library encompasses 2017 EHT measurements as well as future observations with an extended telescope array. We differentiate between robust visibility data products related to model features and data products that are strongly affected by data corruption effects. Parameter inference is mostly limited by intrinsic model variability, which highlights the importance of long-term monitoring observations with the EHT. In later papers in this series, we will show how a Bayesian neural network trained on our synthetic data is capable of dealing with the model variability and extracting physical parameters from EHT observations. With our calibration improvements, our newly reduced EHT datasets have a considerably better quality compared to previously analyzed data. |
| title | Deep learning inference with the Event Horizon Telescope I. Calibration improvements and a comprehensive synthetic data library |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Computational Physics |
| url | https://arxiv.org/abs/2506.13873 |