Filament identification in wide-angle high speed imaging of the mega amp spherical tokamak

Bradley, JW ORCID: 0000-0002-8833-0180 (2019) Filament identification in wide-angle high speed imaging of the mega amp spherical tokamak Review of Scientific Instruments, 90 (9). 093502-. ISSN 0034-6748, 1089-7623

Text 1.5109470.pdf - Published version Download (6MB) | Preview

Abstract

A new tomographic inversion technique is presented for the identification of plasma filaments in wide-angle visible camera data. The technique works on the assumption that background subtracted images of filaments can be represented as a superposition of uniformly emitting magnetic equilibrium field lines. A large collection of equilibrium magnetic field lines is traced and projected onto the camera field of view and combined to form a geometry matrix describing the coordinate transformation from magnetic field aligned coordinates to image pixel coordinates. Inverting this matrix enables the reprojection of the emission in the camera images onto a field aligned basis, from which filaments are readily identifiable. The inversion is a poorly conditioned problem which is overcome using a least-squares approach with Laplacian regularization. Blobs are identified using the "watershed" algorithm and 2D Gaussians are fitted to get the positions, widths, and amplitudes of the filaments. A synthetic camera diagnostic generating images containing experimentally representative filaments is utilized to rigorously benchmark the accuracy and reliability of the technique. 74% of synthetic filaments above the detection amplitude threshold are successfully detected, with 98.8% of detected filaments being true positives. The accuracy with which filament properties and their probability density functions are recovered is discussed, along with sources of error and methods to minimize them.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences, 4601 Applied Computing, 51 Physical Sciences, 5101 Astronomical Sciences, Bioengineering
Depositing User:	Symplectic Admin
Date Deposited:	17 Sep 2019 09:49
Last Modified:	01 Mar 2026 08:41
DOI:	10.1063/1.5109470
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3054937
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.