Quantification and visualization of uncertainties in reconstructed penumbral images of implosions at Omega.

Autor:	Kunimune JH; Plasma Science and Fusion Center, Massachusetts Institute of Technology, 167 Albany St., Cambridge, Massachusetts 02139, USA., Heuer PV; Laboratory for Laser Energetics, University of Rochester, 250 E. River Rd., Rochester, New York 14623, USA., Reichelt BL; Plasma Science and Fusion Center, Massachusetts Institute of Technology, 167 Albany St., Cambridge, Massachusetts 02139, USA., Johnson TM; Plasma Science and Fusion Center, Massachusetts Institute of Technology, 167 Albany St., Cambridge, Massachusetts 02139, USA., Frenje JA; Plasma Science and Fusion Center, Massachusetts Institute of Technology, 167 Albany St., Cambridge, Massachusetts 02139, USA.
Jazyk:	angličtina
Zdroj:	The Review of scientific instruments [Rev Sci Instrum] 2024 Jun 01; Vol. 95 (6).
DOI:	10.1063/5.0214641
Abstrakt:	Penumbral imaging is a technique used in plasma diagnostics in which a radiation source shines through one or more large apertures onto a detector. To interpret a penumbral image, one must reconstruct it to recover the original source. The inferred source always has some error due to noise in the image and uncertainty in the instrument geometry. Interpreting the inferred source thus requires quantification of that inference's uncertainty. Markov chain Monte Carlo algorithms have been used to quantify uncertainty for similar problems but have never been used for the inference of the shape of an image. Because of this, there are no commonly accepted ways of visualizing uncertainty in two-dimensional data. This paper demonstrates the application of the Hamiltonian Monte Carlo algorithm to the reconstruction of penumbral images of fusion implosions and presents ways to visualize the uncertainty in the reconstructed source. This methodology enables more rigorous analysis of penumbral images than has been done in the past. (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)
Databáze:	MEDLINE
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