Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Abstract

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of a family of model distributions defining a functional space within which a solution of the inversion problem is expected to exist. This is a stronger assumption than that underlying traditional methods of tomography and requires that suitable models for the expected distribution be available. In return, the methods offer computationally efficient and robust reconstructions. Regressive tomography, as applied to the data from the JET neutron cameras, involves calculating a set of 100 or more two-dimensional (2-D) neutron emission distributions in a representative variety of conditions using the ASCOT and AFSI Monte Carlo fast ion orbit and fusion reaction codes. The distributions are line integrated to represent synthetic measurements from the 19 channels of this two-camera system. An inversion matrix is then obtained by regressing the 2-D distributions corresponding to each of the voxels against these line integrals. The second method, direct regressive reconstruction, bypasses the calculation of line integrals altogether by regressing experimental camera data against calculated neutron emission distributions. This method does not require the cameras to be calibrated, not even relatively between channels. The inversion matrices obtained by any of the two methods can then be used to provide neutron emission profiles for which ASCOT/AFSI calculations are not available.

Keywords:

• Tomography, fusion plasma
• fusion neutron
• neutron camera
• JET tokamak

Acknowledgment

The views and opinions expressed herein do not necessarily reflect those of the European Commission.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom Research and Training Programme 2014—2018 and 2019—2020 under grant agreement no. 633053.