TY - GEN
T1 - Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements
AU - Levis, Aviad
AU - Lee, Daeyoung
AU - Tropp, Joel A.
AU - Gammie, Charles F.
AU - Bouman, Katherine L.
N1 - Funding Information:
The authors would like to thank George Wong for his help with GRMHD simulations. AL is supported by the Zuckerman and Viterbi postdoctoral fellowships. This work was supported by NSF award 1935980: “Next Generation Event Horizon Telescope Design,” and Beyond Limits, and NSF awards 1743747, 1716327, and 2034306, XSEDE allocation TG-AST170024, and TACC Frontera LSCP AST20023. JAT was supported by ONR BRC Award N00014-18-1-2363 and NSF FRG Award 1952735.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - We develop an approach to recover the underlying properties of fluid-dynamical processes from sparse measurements. We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging. To model the stochastic flow we use spatio-temporal Gaussian Random Fields (GRFs). The high dimensionality of the underlying source video makes direct representation via a GRF's full covariance matrix intractable. In contrast, stochastic partial differential equations are able to capture correlations at multiple scales by specifying only local interaction coefficients. Our approach estimates the coefficients of a space-time diffusion equation that dictates the stationary statistics of the dynamical process. We analyze our approach on realistic simulations of black hole evolution and demonstrate its advantage over state-of-the-art dynamic black hole imaging techniques.
AB - We develop an approach to recover the underlying properties of fluid-dynamical processes from sparse measurements. We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging. To model the stochastic flow we use spatio-temporal Gaussian Random Fields (GRFs). The high dimensionality of the underlying source video makes direct representation via a GRF's full covariance matrix intractable. In contrast, stochastic partial differential equations are able to capture correlations at multiple scales by specifying only local interaction coefficients. Our approach estimates the coefficients of a space-time diffusion equation that dictates the stationary statistics of the dynamical process. We analyze our approach on realistic simulations of black hole evolution and demonstrate its advantage over state-of-the-art dynamic black hole imaging techniques.
UR - http://www.scopus.com/inward/record.url?scp=85124408325&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124408325&partnerID=8YFLogxK
U2 - 10.1109/ICCV48922.2021.00234
DO - 10.1109/ICCV48922.2021.00234
M3 - Conference contribution
AN - SCOPUS:85124408325
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 2320
EP - 2329
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Y2 - 11 October 2021 through 17 October 2021
ER -