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Review
Peer-Review Record

Simulation of Spectra Code (SOS) for ITER Active Beam Spectroscopy

by Manfred von Hellermann 1,*,†, Maarten de Bock 2, Oleksandr Marchuk 1, Detlev Reiter 1, Stanislav Serov 3 and Michael Walsh 2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Submission received: 24 January 2019 / Revised: 13 February 2019 / Accepted: 15 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Plasma Spectroscopy in the Presence of Magnetic Fields)

Round 1

Reviewer 1 Report

I recommend minor editorial changes.

line 118: Add a sentence about recent advances in density-dependent modelling of dielectronic recombination coefficients that are important in any ionization balance models,
see "Suppression of Dielectronic Recombination Due to Finite Density Effects. II. Analytical Refinement and Application to Density-dependent Ionization Balances and AGN Broad-line Emission",D. Nikolić, T. W. Gorczyca, K. T. Korista, M. Chatzikos, G. J. Ferland, F. Guzmán, P A. M. van Hoof, R. J. R. Williams, and N. R. Badnell. The Astrophysical Journal Supplement Series, Volume 237, Number 2 (2018) and whether such effects are or will be incorporated in future revisions of SOS code.


line 129: replace comma after 'ion temperature' with full stop, and start new sentence: '. The impurity ...'
line 181: add comma before ' a systematic ...'
line 190: insert 'of' between 'profiles' and 'electrons'
lines 192 - 194: This sentence is a verbatim copy of the sentence found in lines 165 and 166; remove it.
line 204: caption of Fig 4a, put plasma entry in brackets.
line 209: remove 'used as' in front of 'frequently'
line 224: add comma after 'content'
line 225: add comma after '/s'
line 247, 329, 363: the line of sight path 's' should be italic
line 275: remove 'can be'
line 308, 443, 445:  replace '3-dim', '3d', '3-dimensional' by 3D
line 342: replace 'n' by 'm' in 'inpurity'
line 378: add comma after 'Second'
line 391: replace full stop by colon
line 392: add comma after equation
line 403, 406, 413, 415: fix formatting super- and sub-scripts (for 'CX,tot'  and 'i') as well as '(x)' for capture rate 'sigma'
line 476, 478, 592: put '3' in 3He in superscript
line 490: add 'on the other hand' in between 'density' and 'leads'
line 544:  caption in Fig 23b, 'Wall' should be 'wall'
line 589: 'extend' should be 'extent'

Author Response

please find attached revised version. Your critical comments and suggested editorial corrections are highly appreciated. I have added your reference to the role of dielectronic recombinations to the SOS future strategy section

Author Response File: Author Response.pdf

Reviewer 2 Report

The field of the presented research is of a enormous interest, presented matter is in a sound and understandable form, the referenced papers are helpful in understanding the matter.


The manuscript is a insight into the complex field of fusion plasma spectral line diagnostics and it's usage as a  both diagnostic and control tool. The usage of the Bayesian probability and the usage of the neural network in spectral analysis is a step forward in inclusion of mixture of various processes of spectral line generation and line shape influences. Both Bayesian probability method and neural network are not only dependent onto the used model but also onto the previously analyzed data depth and decision correction. Both of the qualities that presented work possess, the in depth analysis of the underlying processes as well as a long and extensive experimental database.


The interest in hot fusion plasma is growing in it's importance since the underlying research is related to the energetic research. The investigated plasma processes are complex in it's nature and the usage of the spectral analysis as a tool for the control is a normal approach. In such matter a simulation tool enabling the modelling of charge-exchange recombination spectroscopy and associated passive background spectra is needed. The paper presents a deep insight of the applied physical processes and complexities of their implementation in the real usage. The usage of the neural networks just emphasizes the need for the real time response of the simulation software, using it's fitted data as a tool for the control and tracking of the processes inside the fusion deices. The code is in general applicable to any fusion device. The physics background for the underlying emission processes is described, and adequate models for the active and passive CXRS emission, continuum radiation, edge line emission, halo and plume effect, and charge exchange cross-section effects on line shapes are described and presented. All of the analyzed process are complex in both experimental data collection and theoretical process analysis, there are plasma sheets with orders of magnitude differences in their parameters.


The most modern and most accurate approach of synthetic spectra is used in the base of the analysis. The quality of the simulated data could be seen in the comparison of the experimental profiles with the simulated data, the achieved results are extraordinary. Mentioned disagreement in analysis of the line shapes between the experimental data and several theoretical model results is just an additional emphasis onto the importance of further in depth analysis and better simulation models for the underlying theory. As it is known the process of deconvollution of the experimental spectra is a tedious and sensitive process. It is sometimes hard to identify the real path for the spectral line shape influence, especially in the case of complex systems, with a large optical paths and a not simple plasma composition, and after all a several orders of magnitude variations of plasma parameters onto the underlying optical path. The complexity of both experimental and theoretical knowledge of the entire processes inside the reactor is of extraordinary importance. The profiles of the plasma parameters relies on collected data from the variety of sensors for plasma diagnostics, and the optical path layout for the spectral analysis, with all of its errors, influence the quality of collected parameter and spectral data. On the other hand, knowledge of entire processes and their cross sections, e.g. the theoretical and experimental data agreement, and the knowledge of the importance of the main branch processes is needed. The need of fast response of the joint data collection and analysis is a problem itself and the machine learned experience in combination with a human correction of an neural network system is a must have. The used spectral databases and models are by itself a state of the art in the field, and the complexity of the modeling procedure of the mixed processes influence is another step further. The work itself presents a peak of a both the experimental and the theoretical knowledge

Author Response

Your very positive assessment is highly appreciated, please find attached the final version which contains also some update of figures linked to the halo modelling

Author Response File: Author Response.pdf

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