Pith. sign in

REVIEW 3 major objections 6 minor 1 cited by

Integrated Data Analysis and Validation

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Bayesian combination of all diagnostics yields more reliable fusion plasma reconstructions than sequential analysis.

desk verdict A competent review of Bayesian integrated data analysis; the ITER demo is self-consistent but untested against misspecified models, and the central claim rests on prior real-data applications. read the letter →

arxiv 2411.09270 v1 pith:EJ4CQ3VK submitted 2024-11-14 physics.plasm-ph

R. Fischer , A. Bock , S. S. Denk , A. Medvedeva. M. Salewski , M. Schneider , D. Stieglitz , ASDEX Upgrade Team This is my paper · ORCID
classification physics.plasm-ph
keywords integrateddataanalysisBayesianinferenceplasmadiagnosticsforwardmodelsuncertaintyquantificationprofilereconstructionvelocity-spacetomographyfusionplasmas
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the standard way of analysing fusion diagnostics — fitting each diagnostic separately and then combining the results — should be replaced by Integrated Data Analysis (IDA), a one-step Bayesian combination of all measurements, forward models, and physics prior information. In IDA, each diagnostic contributes a likelihood that compares the measured data with a forward model of the measurement, and the joint posterior over the physical parameters of interest is formed by multiplying those likelihoods together with priors. The claim is that this coherent product yields more reliable parameter estimates, fully propagated uncertainties, and genuine synergistic effects: information from one diagnostic can sharpen parameters that another diagnostic measures only indirectly through their correlations. A sympathetic reader would care because present and next-generation fusion devices such as ITER and DEMO need self-consistent profiles, equilibria, and fast-ion distributions for control, safety, and physics, and because the same framework provides a quantitative way to detect inconsistent data and degrading calibration. The paper demonstrates the workflow on a synthetic ITER diagnostic set and reviews applications to profile reconstruction, equilibrium reconstruction, and velocity-space tomography.

What carries the argument

The load-bearing object is the posterior probability distribution $p(f|d)$ over the physical parameters $f$ given all data $d$, together with the product likelihood that assembles it. The machinery has four named ingredients: forward models $D_k(f)$, which compute synthetic measurements from parameters; likelihoods $p_k(d_k|f)$, typically Gaussian or Student's $t$, which quantify data uncertainties including outliers; priors $p(f)$, which encode non-negativity, smoothness, monotonicity, or physics-based constraints; and nuisance parameters, whose priors are marginalised so that systematic uncertainties flow into the final uncertainties. For velocity-space tomography the forward model takes the explicit matrix form $S = W F$, where $W$ holds the velocity-space weight functions of all detectors, and the inversion is a regularised least-squares problem with constraints such as non-negativity. The same posterior-based machinery carries every application in the paper: it converts ill-posed inversion into well-posed probabilistic inference, and it automatically propagates all uncertainties.

What would settle it

Run a realistic comparison on experimental data: for a set of discharges with overlapping diagnostics and an independent reference profile, withhold one diagnostic, perform the joint Bayesian inversion with the remaining ones, and count how often the withheld data fall within the posterior-predicted uncertainty bands; if the claimed improvement is real, the joint posterior should predict the withheld diagnostic at the nominal rate, while a deliberately misspecified forward model should produce systematic predictive failures.

Watch

Extended reading notes

Core claim

In the paper's own terms, the central discovery is that a joint Bayesian analysis of heterogeneous diagnostics — rather than a sequential one — is the correct way to combine information, because every measurement and every piece of modelling knowledge is subject to uncertainty and should be processed by the same probability rules. Bayes' theorem $p(f|d) \propto p(d|f)\,p(f)$ is expanded so that the likelihood is the product over diagnostics, $p(d|f)=\prod_k p_k(d_k|f)$, with each $p_k$ built from a forward model $D_k(f)$ and an uncertainty model for that diagnostic. Systematic effects become nuisance parameters with prior distributions that are marginalised, so their uncertainty propagates into the parameters of interest instead of being ignored. The paper shows that this product structure produces correlations that a sequential analysis discards, illustrated by a 30 percent reduction in the uncertainty of the electron density marginal when soft X-ray data, which carry no direct density information, are combined with Thomson scattering data. Within this framework, profile reconstruction, equilibrium reconstruction, and velocity-space tomography of fast ions are all treated as the same type of probabilistic inversion, with forward modelling and priors replacing ad hoc regularisation.

Load-bearing premise

The assumption the whole claim rests on is that every forward model and every stated uncertainty is accurate enough that the product of likelihoods is not dominated by one misspecified model; the paper itself notes that successful probabilistic validation does not prove physical correctness, and its ITER demonstration uses synthetic data generated by the same forward models used for the inversion.

Editorial extensions

If this is right

  • If IDA is correct, plasma profiles such as electron density, temperature, and effective charge can be reconstructed with smaller and more honest uncertainties than separate per-diagnostic fits followed by a linking step.
  • Diagnostics interdependencies become an asset: a measurement that constrains only a combination of parameters can reduce the uncertainty of each parameter once correlations are kept in the posterior, as in the Thomson-scattering/soft-X-ray example.
  • Outlier-robust likelihoods and marginalised nuisance parameters give a quantitative procedure for detecting inconsistent diagnostics and for early detection of optical degradation, which matters for long-pulse and remote operation.
  • Coupling profile estimation with equilibrium reconstruction in one alternating Bayesian scheme improves both, and the iteration converges in a few steps, with equilibrium uncertainties propagated through a Monte-Carlo sampling of equilibria.
  • Velocity-space tomography with combined fast-ion diagnostics suppresses artifacts that single-diagnostic inversions produce, and makes feasible measurements of alpha-particle distribution functions at ITER from combined gamma-ray and collective Thomson scattering data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An extension left implicit is that IDA turns calibration into a continuous, data-driven process: if calibration constants are treated as nuisance parameters, their posterior distributions over many discharges give an automatic drift monitor for optical components without dedicated calibration campaigns.
  • A testable cross-validation scheme follows from the same logic: withhold one diagnostic from the joint analysis, predict its data from the rest, and check predictive coverage; this would quantify the cost of forward-model misspecification on real data, which the paper's synthetic ITER example does not test.
  • The same weight-function formalism used for velocity-space tomography could be applied to detector design: choosing diagnostic geometries to maximise complementarity of their $W$ matrices, rather than individual measurement accuracy, would optimise a diagnostic set for joint inference.
  • Although the paper is framed for fusion, the underlying recipe — multiply likelihoods of heterogeneous sensors, add physical priors, marginalise nuisance parameters — transfers to any remote-sensing or multi-sensor inversion problem with trustworthy forward models.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents Integrated Data Analysis (IDA) as a Bayesian framework for combining heterogeneous diagnostics and modelling information in fusion plasmas. It reviews the components: Bayes' theorem, forward models, uncertainty quantification, likelihoods, priors, parameterization, estimation methods, validation, and numerical implementation; describes IMAS integration; and gives examples: a synergistic TS+SXR illustration, an ITER synthetic profile-reconstruction demo combining ECE, TS, and TIP, equilibrium reconstruction at ASDEX Upgrade, and velocity-space tomography. The authors argue IDA improves reliability, resolution, and handling of ill-posed inversions compared with sequential single-diagnostic analysis.

Significance. As a review of established practice, the paper is valuable: it consolidates a coherent methodology applied at W7-AS, ASDEX Upgrade, JET, TJ-II, MST, W7-X, and EAST, and it gives a practical recipe with explicit equations. The mathematical exposition is standard Bayesian theory and is sound. The paper's explicit acknowledgment that successful probabilistic validation does not imply physical correctness is a responsible limitation. The main caveat is that the only new quantitative example, the ITER profile demo, is a self-consistency check and does not by itself establish improvement over sequential analysis under model misspecification. With a clearer statement of that limitation, the claims are appropriately scoped.

major comments (3)
  1. [Section 4.2, Fig. 5] The agreement between the MAP/MCMC reconstructions and the 'original' profiles is expected from construction because the synthetic ECE, TS, and TIP data were generated from the same forward models and noise distributions used in the inversion. This makes the demo a self-consistency check of the software, not a validation of IDA against model misspecification or a demonstration of improvement over a sequential baseline. Please state this explicitly in Section 4.2 and temper the corresponding phrasing in the Summary.
  2. [Section 4.2 vs Section 2.2] The demo uses the black-body ECE forward model (Trad = Te), whereas Section 2.2 notes that ITER ECE requires radiation-transport modeling (ECRad) because of optically thin emission and harmonic overlap. The demo therefore avoids the failure mode most relevant to the target device. Please either use the high-fidelity ECE forward model in the example or explicitly label the demo as a low-fidelity feasibility test and discuss how ECE model misspecification would affect the joint posterior.
  3. [Section 2.8] Because the posterior is proportional to the product of likelihoods (Section 2.1), a biased forward model on one diagnostic can dominate the joint result and produce overconfident estimates. The validation section would benefit from a sentence on this risk, along with practical checks such as posterior predictive residuals or comparisons with sequential single-diagnostic analyses on real data. This would more directly support the 'Validation' in the title.
minor comments (6)
  1. [Section 2.5] 'first-oder' should be 'first-order' in the list of Tikhonov regularizers.
  2. [Section 2.9 vs Section 4.2] Section 2.9 says the IDA toolbox is 'presently under development', while Section 4.2 says it 'was developed'; please reconcile the status.
  3. [Section 4.2] 'Random noise of 10% for both ECE and TS data were added' should be 'was added'.
  4. [Section 4.2, Fig. 4] The description 'the data are normalized to the lengths of the LOSs' should clarify whether the line-integrated TIP data are divided by chord length to obtain a meaningful average density or merely scaled for plotting.
  5. [Figure 2] The caption should state the location and scale parameters for the Gaussian and Cauchy curves so the comparison is well defined.
  6. [Reference [8]] The author list includes 'JET-EFDA Contributors' in an odd position; this appears to be a formatting error.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a review of Bayesian IDA, and the synthetic ITER demo is a disclosed self-consistency check rather than a load-bearing derivation.

full rationale

This manuscript is a review/tutorial of Bayesian Integrated Data Analysis, not an original derivation in which a predicted quantity is obtained from first principles. The central claim—that coherent probabilistic combination of heterogeneous diagnostics improves reliability and uncertainty quantification—is supported by references to applications on multiple devices, including independent groups (JET [3], TJ-II [7], MST [8,9], W7-X [12,13]), so it is not carried by a self-citation chain. The only place resembling a 'prediction' is the ITER profile-reconstruction example in Section 4.2. There, synthetic ECE, TS, and TIP data are generated from the same forward models and noise assumptions used in the likelihood, so the MAP/MCMC agreement with the 'original' profiles is a self-consistency check of the inversion machinery rather than an external validation. This is a limitation for model-misspecification robustness, but the paper explicitly labels it 'this test example' and in Section 2.8 states that 'successful probabilistic validation does not imply a physically correct description of the data and correct physical modelling,' thereby disclosing rather than concealing the limitation. The velocity-space tomography examples use measured spectra and identify the regularization strength λ as a free parameter with open bias-uncertainty questions, so no fitted parameter is renamed as a prediction. No equation is defined in terms of its target, and no load-bearing argument reduces to an unverified self-citation. Therefore no significant circularity is found.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central dependency is on the assumed validity of forward models and uncertainty representations, plus user-chosen regularization parameters. The free parameters listed are genuine degrees of freedom in the presented workflow, with no unique values provided.

free parameters (4)
  • Tikhonov regularization strength lambda (L1, L0) = Not fixed; selected per case via L-curve or generalized cross validation, typically within a factor 10
    In velocity-space tomography, Eq. (8) and Eq. (12) balance data fitting against smoothing. The paper states 'lambda is a free parameter of the problem that must be determined as part of the solution' and that no selection method is always advantageous (Section 4.4.3).
  • Student's t likelihood degree parameter a = Not specified
    In Eq. (3) the outlier-robust likelihood is controlled by a. Section 4.2 says the Student's t-likelihood for ECE and TS data was 'arbitrarily chosen', and the value of a is not given.
  • Monotonicity tolerance sigma_m = Not specified
    The monotonicity penalty prior in Eq. (4) depends on sigma_m, which quantifies tolerance for wrong-sign gradients; the paper gives no default or calibration procedure.
  • Spline knot number and locations (profile parameterization) = Not specified
    Section 2.6 notes that the number and position of spline knots determines spatial resolution, but the ITER demo in Section 4.2 does not state the knot scheme used, making the reconstruction setup underdetermined.
assumptions (5)
  • standard math Bayes' theorem and probability calculus as the framework for uncertain inference.
    Used throughout Section 2.1 as the foundation of IDA; no proof needed, standard mathematical background.
  • domain assumption Forward models of each diagnostic provide an accurate mapping from physical parameters to measured data, up to quantifiable uncertainties.
    Section 2.2 assumes forward models of varying fidelity (e.g., black-body ECE, radiation transport ECRad, CXRS spectrum models) are available and correct enough for the likelihood to be valid.
  • domain assumption Measurement and systematic uncertainties can be represented by likelihoods or prior distributions (Gaussian, Student's t, Poisson) with known or learnable parameters.
    Section 2.3 and 2.4 rely on this to define the likelihood pdfs; if uncertainty models are misspecified, posterior estimates and validation residuals are not meaningful.
  • ad hoc to paper Physical prior information (smoothness, non-negativity, monotonicity, slowing-down physics) is justified for the reconstructed plasma parameters.
    Sections 2.5 and 4.4.3 impose these priors to regularize ill-posed inversions; the paper acknowledges that the choice of prior weight and form affects results and can miss real features (e.g., local minima in distribution functions).
  • domain assumption In velocity-space tomography, null-measurements (measured absence of fast ions) indicate genuinely empty velocity-space regions.
    Section 4.4.3 uses null-measurements to restrict the velocity space (Eq. 11, Fig. 8); if the null is a sensitivity artifact rather than true absence, the constraint biases the reconstruction.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Integrated Data Analysis and Validation." pith.science (2026). https://pith.science/paper/EJ4CQ3VK

@misc{pith2026241109270,
  author       = {Pith},
  title        = {Pith review of: Integrated Data Analysis and Validation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EJ4CQ3VK}},
  note         = {Machine review of arXiv:2411.09270}
}
read the original abstract

A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modelling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g., to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modelling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of Integrated Data Analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.

Figures

Figures reproduced from arXiv: 2411.09270 by the authors.

Figure 1
Figure 1. Simplified flow-charts for typical data analysis steps inferring electron temperature Te and density ne profiles for magnetic confinement fusion experiments from the Thomson scattering and electron cyclotron emission (ECE) diagnostics in (a) a conventional approach and (b) within the IDA concept. of the estimation uncertainties. Data and result validation and overall consistency checks between coupled diagnostics mi… view at source ↗
Figure 2
Figure 2. Comparison of a Gaussian with a Cauchy distribution appropriate for outlier robust estimation than the Gaussian pdf ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Synergistic effect by exploiting full probability distribution part for numerical efficiency. 4. Examples 4.1. Synergistic effect The result of a Bayesian analysis is a probability distribution of the parameters of interest. In case of a multidimensional probability distribution, the pdf contains the dependencies between the parameters. These dependencies allow one to obtain a synergistic effect where the result of … view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Toroidal plane of ITER (major radius 6.2 m, minor radius 2 m) and the 5 TIP LOSs data are normalized to the lengths of the LOSs as shown in figure 4. The 5 TIP data are plotted at arbitrary plasma position sorted according to the smallest (largest) length of the LOS to…
Figure 5
Figure 5. Figure 5: Simulated (black solid line) and reconstructed (red solid line) temperature and density profiles estimated from the noisy data from the ECE (green), TS (blue) and TIP (orange) diagnostics. The open diamonds depict the forward modelled TIP data using the fitted density …
Figure 6
Figure 6. Figure 6: Measurement of a fast-ion velocity distribution function [a.u.] in the center of a plasma heated by co-current and counter-current neutral beam injection at EAST [54]. The tomographic inversion is based on (a) FIDA spectra using two detectors, and (b) additionally a NE…
Figure 7
Figure 7. Figure 7: Exemplary weight function showing the velocity￾space sensitivity of a CTS measurement at a particular Doppler shift. have been developed for all major fast-ion diagnostics: FIDA [72, 73], neutral particle analyzers (NPA) [72], CTS [74], NES [75, 76], GRS [77, 78] and f…
Figure 8
Figure 8. Figure 8: The colored lines are boundaries of weight functions connected to null-measurements. The black line is their envelope, presenting a boundary to the velocity space region empty of fast ions [59]. with non-negativity, restricted velocity space, and monotonicity constrain…
Figure 10
Figure 10. Figure 10: Prior information of unlikely velocity space for velocity-space tomography at MAST according to (a) TRANSP/NUBEAM and (b) null-measurements [65]. The monotonously growing κ0(E, p) towards higher energies ex￾presses our increasing doubt to find ions. space and the targ…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Simultaneous kinetic profile and magnetic equilibrium inference with Bayesian integrated data analysis in preparation for ITER

    physics.plasm-ph 2025-02 conditional novelty 6.0 of 10

    A Bayesian framework simultaneously reconstructs kinetic profiles and magnetic equilibrium from simulated ITER diagnostics, giving MAP results with uncertainties in about three minutes that mostly agree with MCMC veri...

Reference graph

Works this paper leans on

91 extracted references · 79 canonical work pages · cited by 1 Pith paper

  1. [1]

    Fischer, A

    R. Fischer, A. Dinklage, and E. Pasch. Bayesian modelling of fusion diagnostics. Plasma Phys. Control. Fusion , 45:1095–1111, 2003

    2003

  2. [2]

    D. Dodt, A. Dinklage, R. Fischer, K. Bartschat, O. Zatsarinny, and D. Loffhagen. Reconstruction of an electron energy distribution function using integrated data analysis. J. Phys. D: Appl. Phys. , 41:205207, 2008

    2008

  3. [3]

    O. Ford, J. Svensson, M. Beurskens, A. Boboc, J. Flanagan, M. Kempenaars, D.C. McDonald, A. Meakins, and JET EFDA Contributors. Bayesian combined analysis of JET LIDAR, edge LIDAR and interferometry diagnostics. In M. Mateev and E. Benova, editors, EPS 2009 / Europhysics Conference Abstracts, volume 33E, pages P– 2.150. European Physical Society, Geneva, 2009

    2009

  4. [4]

    D. Dodt, R. Fischer, A. Korotkov, T. Eich, and JET- EFDA contributors. Electron density profiles from the probabilistic analysis of the lithium beam at JET. In EPS 2009 / Europhysics Conference Abstracts , volume 33E, pages P–2.148. 2009

    2009

  5. [5]

    Fischer, C.J

    R. Fischer, C.J. Fuchs, B. Kurzan, W. Suttrop, E. Wolfrum, and ASDEX Upgrade Team. Integrated data analysis of profile diagnostics at ASDEX Upgrade. Fusion Sci. Technol., 58:675–684, 2010

    2010

  6. [6]

    Rathgeber, R

    S.K. Rathgeber, R. Fischer, S. Fietz, J. Hobirk, A. Kallen- bach, H. Meister, T. P¨ utterich, F. Ryter, G. Tardini, E.Wolfrum, and the ASDEX Upgrade Team. Estimation of profiles of the effective ion charge at ASDEX Upgrade with Integrated Data Analysis. Plasma Phys. Control. Fusion, 52:095008, 2010

    2010

  7. [7]

    van Milligen, T

    B.Ph. van Milligen, T. Estrada, E. Ascas ´ ıbar, D. Tafalla, D. L´ opez-Bruna, A.˜L´ opez Fraguas, J.A. Jim´ enez, I. Garc ´ ıa- Cort´ es, A. Dinklage, and R. Fischer. Integrated data analysis at TJII: The density profile. Rev. Sci. Instrum., 82:073503, 2011

    2011

  8. [8]

    L. M. Reusch, M. E. Galante, P. Franz, J. R. Johnson, M. B. McGarry, H. D. Stephens, , and D. J. Den Hartog JET-EFDA Contributors. An integrated data analysis tool for improving measurements on the MST RFP. Rev. Sci. Instrum. , 85:11D844, 2014

    2014

Show all 91 references
  1. [9]

    Galante, L.M

    M.E. Galante, L.M. Reusch, D.J. Den Hartog, P. Franz, J.R. Johnson, M.B. McGarry, M.D. Nornberg, and H.D. Stephens. Determination of Zeff by integrating measurements from x-ray tomography and charge exchange recombination spectroscopy. Nucl. Fusion , 55:123016, 2015

    2015

  2. [10]

    Salewski et al

    M. Salewski et al. Bayesian integrated data analysis of fast- ion measurements by velocity-space tomography. Fusion Sci. Tech., 74:23–36, 2018

    2018

  3. [11]

    Fischer, L

    R. Fischer, L. Giannone, J. Illerhaus, P.J. McCarthy, R.M. McDermott, and ASDEX Upgrade Team. Estimation and uncertainties of profiles and equilibria for fusion modeling codes. Fusion Sci. Technol., 76:879–893, 2020

    2020

  4. [12]

    S. Kwak, U. Hergenhahn, U. H¨ ofel, M. Krychowiak, A. Pavone, J. Svensson, O. Ford, R. K¨ onig, S. Bozhenkov, G. Fuchert, E. Pasch, K.J. Brunner, J. Knauer, P. Kornejew, H. Trimino Mora, T.S. Ped- ersen, and Wendelstein 7-X Team. Bayesian inference of spatially resolved Zeff p...

    2021

  5. [13]

    S. Kwak, J. Svensson, S. Bozhenkov, H. Trimino Mora, U. H¨ ofel, A. Pavone, M. Krychowiak, A. Langenberg, Integrated Data Analysis and Validation 19 Y. c. Ghim, and W7-X Team. Bayesian modelling of multiple plasma diagnostics at Wendelstein 7-X. arXiv:2103.07582, 2021

    2021 arXiv

  6. [14]

    Fischer and A

    R. Fischer and A. Dinklage. The concept of Integrated Data Analysis of complementary experiments. In K.H. Knuth, editor, Bayesian Inference and Maximum Entropy Methods in Science and Engineering , volume Conf. Proc. 954, pages 195–202. AIP, Melville, NY, 2007

    2007

  7. [15]

    Fischer and A

    R. Fischer and A. Dinklage. Integrated data analysis of fusion diagnostics by means of the Bayesian probability theory. Rev. Sci. Instrum. , 75:4237–4239, 2004

    2004

  8. [16]

    Fischer, C

    R. Fischer, C. Wendland, A. Dinklage, S. Gori, V. Dose, and the W7-AS team. Thomson scattering analysis with the Bayesian probability theory. Plasma Phys. Control. Fusion, 44:1501–1519, 2002

    2002

  9. [17]

    Fischer, E

    R. Fischer, E. Wolfrum, J. Schweinzer, and the ASDEX Upgrade Team. Probabilistic lithium beam data analysis. Plasma Phys. Control. Fusion , 50:1–26, 2008

    2008

  10. [18]

    Svensson, A

    J. Svensson, A. Dinklage, J. Geiger, A. Werner, and R. Fischer. Integrating diagnostic data analysis for W7- AS using Bayesian graphical models. Rev. Sci. Instrum., 75:4219–4221, 2004

    2004

  11. [19]

    L. M. Reusch, M. D. Nornberg, J. A. Goetz, and D. J. Den Hartog. Using integrated data analysis to extend measurement capability. Rev. Sci. Instrum., 89:10K103, 2018

    2018

  12. [20]

    Toussaint

    U.v. Toussaint. Bayesian inference in physics. Rev. Mod. Phys., 83:943, 2011

    2011

  13. [21]

    S. S. Denk, R. Fischer, H. M. Smith, P. Helander, O. Maj, E. Poli, J. Stober, U. Stroth, W. Suttrop, E. Westerhof, M. Willensdorfer, and the ASDEX Upgrade Team. Analysis of electron cyclotron emission with extended electron cyclotron forward modeling. Plasma Phys. Control. Fus...

    2018

  14. [22]

    S. S. Denk, R. Fischer, E. Poli, O. Maj, S. K. Nielsen, J. Rasmussen, M. Stejner, M. Willensdorfer, and the ASDEX Upgrade Team. ECRad: An electron cyclotron radiation transport solver for advanced data analysis in thermal and non-thermal fusion plasmas. Commun. Comput. Phys. ,...

    2020

  15. [23]

    S. S. Denk, R. Fischer, E. Westerhof, T. Luda di Cortemiglia, J. Hobirk, O. Maj, E. Poli, S. K. Nielsen, J. Rasmussen, M. Stejner, J. Stober, U. Stroth, W Sut- trop, M. Willensdorfer, and the ASDEX Upgrade Team. Momentum-space-resolved measurements using oblique electron cyclo...

    2021

  16. [24]

    R. M. McDermott, A. Lebschy, B. Geiger, C. Bruhn, M. Cavedon, M. Dunne, R. Dux, R. Fischer, A. Kap- patou, T. P¨ utterich, E. Viezzer, and ASDEX Upgrade Team. Extensions to the charge exchange recombina- tion spectroscopy diagnostic suite at ASDEX Upgrade. Rev. Sci. Instrum. ,...

    2017

  17. [25]

    BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, 2008

    Evaluation of measurement data – Guide to the expression of uncertainty in measurement, volume JCGM 100:2008. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, 2008

    2008

  18. [26]

    Dose and W

    V. Dose and W. von der Linden. Outlier tolerant parameter estimation. In W. von der Linden, V. Dose, R. Fischer, and R. Preuss, editors, Maximum Entropy and Bayesian Methods. Kluwer Academic Publishers, Dordrecht, 1999

    1999

  19. [27]

    Rathgeber, R

    S.K. Rathgeber, R. Fischer, S. Fietz, J. Hobirk, A. Kallen- bach, H. Meister, T. P¨ utterich, F. Ryter, G. Tardini, E.Wolfrum, and the ASDEX Upgrade Team. Estima- tion of edge electron temperature profiles via forward model/afs/ipp/home/r/rrf/bb.epsling of the electron cy- clo...

    2013

  20. [28]

    Wendler, R

    D. Wendler, R. Dux, R. Fischer, M. Griener, E. Wol- frum, G. Birkenmeier, U. Stroth, and the ASDEX Up- grade Team. Collisional radiative model for the evalua- tion of the Thermal Helium Beam diagnostic at ASDEX Upgrade. Plasma Phys. Control. Fusion , 64:045004, 2022

    2022

  21. [29]

    Chilenski, M

    M.A. Chilenski, M. Greenwald, Y. Marzouk, N.T. Howard, A.E. White, J.E. Rice, and J.R. Walk. Improved profile fitting and quantification of uncertainty in experimental measurements of impurity transport coefficients using Gaussian process regression. Nucl. Fusion , 55:023012, 2015

    2015

  22. [30]

    T. Wang, D. Mazon, J. Svensson, D. Li, A. Jardin, and G. Verdoolaege. Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information. Rev. Sci. Instrum. , 89:063505, 2018

    2018

  23. [31]

    Fischer, K

    R. Fischer, K. M. Hanson, V. Dose, and W. von der Linden. Background estimation in experimental spectra. Phys. Rev. E, 61:1152, 2000

    2000

  24. [32]

    Imbeaux, S.D

    F. Imbeaux, S.D. Pinches, J.B. Lister, Y. Buravand, T. Casper, B. Duval, B. Guillerminet, M. Hosokawa, W. Houlberg, P. Huynh, S.H. Kim, G. Manduchi, M. Owsiak, B. Palak, M. Plociennik, G. Rouault, O. Sauter, and P. Strand. Design and first applications of the ITER integrated m...

    2015

  25. [33]

    Van Zeeland, R.L

    M.A. Van Zeeland, R.L. Boivin, D.L. Brower, T.N. Carl- strom, J.A. Chavez, W.X. Ding, R. Feder, D. Johnson, L. Lin, R.C. O’Neill, and C. Watts. Conceptual de- sign of the tangentially viewing combined interferometer- polarimeter for iter density measurements. Rev. Sci. In- str...

    2013

  26. [34]

    A. Bock, E. Fable, R. Fischer, M. Reich, D. Rittich, J. Stober, M. Bernert, A. Burckhart, H. Doerk, M. Dunne, B. Geiger, L. Giannone, V. Igochine, A. Kappatou, R. McDermott, A. Mlynek, T. Odstrˇ cil, G. Tardini, H. Zohm, and the ASDEX Upgrade Team. Non-inductive improved H-mod...

    2017

  27. [35]

    A. Bock, H. Doerk, R. Fischer, D. Rittich, J. Stober, A. Burckhart, E. Fable, B. Geiger, A. Mlynek, M. Re- ich, H. Zohm, and the ASDEX Upgrade Team. Ad- vanced tokamak investigations in full-tungsten ASDEX Upgrade. Physics of Plasmas , 25:056115, 2018

    2018

  28. [36]

    McCarthy, W

    P.J. McCarthy, W. Schneider, and P. Martin. The CLISTE interpretive equilibrium code. Technical Report IPP5/85, Max-Planck-Institut f¨ ur Plasmaphysik, Garching, 1999

    1999

  29. [37]

    McCarthy

    P.J. McCarthy. Identification of edge-localized moments of the current density profile in a tokamak equilibrium from external magnetic measurements. Plasma Phys. Control. Fusion, 54:015010, 2012

    2012

  30. [38]

    L. L. Lao, H. E. St. John, Q. Peng, J. R. Ferron, E. J. Strait, T. S. Taylor, W. H. Meyer, C. Zhang, and K. I. You. MHD equilibrium reconstruction in the DIII-D tokamak. Fusion Science and Technology, 48(2):968–977, 2005

    2005

  31. [39]

    M. Brix, N. Hawkes, A. Boboc, V. Drozdov, and S. Shara- pov. Accuracy of EFIT equilibrium reconstruction with internal diagnostic information at JET. The Review of scientific instruments , 79:10F325, 11 2008

    2008

  32. [40]

    J. Blum, C. Boulbe, and B. Faugeras. Reconstruction of the equilibrium of the plasma in a tokamak and identification of the current density profile in real time. J. Comput. Phys., 231:960–980, 2012

    2012

  33. [41]

    Fischer, A

    R. Fischer, A. Bock, M. Dunne, J.C. Fuchs, L. Giannone, K. Lackner, P.J. McCarthy, E. Poli, R. Preuss, M. Rampp, M. Schubert, J. Stober, W. Suttrop, G. Tardini, M. Weiland, and ASDEX Upgrade Team. Coupling of the flux diffusion equation with the equilibrium reconstruction at A...

    2016

  34. [42]

    Fischer, A

    R. Fischer, A. Bock, A. Burckhart, O.P. Ford, L. Giannone, V. Igochine, M. Weiland, M. Willensdorfer, and ASDEX Upgrade Team. Sawtooth induced q-profile evolution at ASDEX Upgrade. Nucl. Fusion, 59:056010, 2019

    2019

  35. [43]

    Rampp, R

    M. Rampp, R. Preuss, R. Fischer, and the ASDEX Upgrade Team. GPEC: A real-time capable tokamak equilibrium code. Fusion Sci. Technol. , 70(1):1–13, 2016

    2016

  36. [44]

    Giannone, R

    L. Giannone, R. Fischer, A. Kappatou, G. Tardini, M. Weiland, C. Angioni, E. Fable, M. Griener, R.M. McDermott, B. Sieglin, A. Jansen van Vuuren, R. Bilato, M. Dunne, A. Gude, A. Kallenbach, J.M. Kurz, M. Maraschek, D.M. Rittich, F. Ryter, P.A. Schneider, K.H. Schuhbeck, U. St...

    2021

  37. [45]

    Weiland, R

    M. Weiland, R. Bilato, R. Dux, B. Geiger, A. Lebschy, F. Felici, R. Fischer, D. Rittich, M. Van Zeeland, the ASDEX Upgrade team, and the Eurofusion MST1 team. RABBIT: Real-time simulation of the NBI fast-ion distribution. Nucl. Fusion, 58:082032, 2018

    2018

  38. [46]

    Budny, M.G

    R.V. Budny, M.G. Bell, H. Biglari, M. Bitter, C.E. Bush, C.Z. Cheng, E.D. Fredrickson, B. Grek, K.W. Hill, H. Hsuan, A.C. Janos, D.L. Jassby, D.W. Johnson, L.C. Johnson, B. LeBlanc, D.C. McCune, D.R. Mikkelsen, H.K. Park, A.T. Ramsey, S.A. Sabbagh, S.D. Scott, J.F. Schivell, J...

    1992

  39. [47]

    Burckhart, O

    A. Burckhart, O. Ford, M. Reich, R. Wolf, and the ASDEX Upgrade Team. Design of the new Imaging Motional Stark Effect diagnostic at ASDEX Upgrade. In R. Bingham, Suttrop, S. Atzeni, R. Foest, K. McClements, B. Goncalves, C. Silva, and R. Coelho, editors, EPS 2015 / Europhysics...

    2015

  40. [48]

    Mlynek, R

    A. Mlynek, R. Fischer, O. Ford, P. Lang, B. Pl¨ ockl, and ASDEX Upgrade Team. First results from the new sub- millimeter polarimeter on the ASDEX Upgrade tokamak. In 21st Topical Conference on High Temperature Plasma Diagnostics (HTPD 2016) , 2016

    2016

  41. [49]

    Fischer, J

    R. Fischer, J. Hobirk, L. Barrera, A. Bock, A. Burckhart, I. Classen, M. Dunne, J.C. Fuchs, L. Giannone, K. Lackner, P.J. McCarthy, E. Poli, R. Preuss, M. Rampp, S.K. Rathgeber, M. Reich, B. Sieglin, W. Suttrop, E. Wolfrum, and ASDEX Upgrade Team. Magnetic equilibrium reconstr...

    2013

  42. [50]

    Fischer, L

    R. Fischer, L. Giannone, K. Lackner, R.M. McDermott, E. Viezzer, H.P. Zehrfeld, and ASDEX Upgrade Team. Effect of measured toroidal flows on tokamak equilibria. In 42th EPS Conference on Plasma Physics , page P1.117. European Physical Society, Geneva, 2015

    2015

  43. [51]

    Salewski et al

    M. Salewski et al. Tomography of fast-ion velocity- space distributions from synthetic CTS and FIDA measurements. Nucl. Fusion , 52(10):103008, October 2012

    2012

  44. [52]

    Salewski et al

    M. Salewski et al. Combination of fast-ion diagnos- tics in velocity-space tomographies. Nucl. Fusion , 53(6):063019, June 2013

    2013

  45. [53]

    Salewski et al

    M. Salewski et al. Measurement of a 2D fast-ion velocity distribution function by tomographic inversion of fast-ion D-alpha spectra. Nucl. Fusion, 54(2):023005, February 2014

    2014

  46. [54]

    Madsen et al

    B. Madsen et al. Tomography of the positive-pitc fast- ion velocity distribution in DIII-D plasmas with Alfv´ en eigenmodes and neoclassical tearing modes. Nucl. Fusion, 60:066024, 2020

    2020

  47. [55]

    Moseev, M

    D. Moseev, M. Salewski, M. Garcia-Munoz, B. Geiger, and M. Nocente. Recent progress in fast-ion diagnostics for magnetically confined plasmas. Rev. Mod. Plasma Phys., 2:7, December 2018

    2018

  48. [56]

    Eriksson et al

    J. Eriksson et al. Measuring fast ions in fusion plasmas with neutron diagnostics at JET. Plasma Phys. Control. Fusion, 61:014027, 2019

    2019

  49. [57]

    Salewski et al

    M. Salewski et al. MeV-range velocity-space tomography from gamma-ray and neutron emission spectrometry measurements at JET. Nucl. Fusion, 57:056001, 2017

    2017

  50. [58]

    Dual sightline measurements of MeV range deuterons with neutron and gamma-ray spectroscopy at JET

    J Eriksson et al. Dual sightline measurements of MeV range deuterons with neutron and gamma-ray spectroscopy at JET. Nucl. Fusion, 55(12):123026, 2015

    2015

  51. [59]

    Salewski et al

    M. Salewski et al. High-definition velocity-space tomogra- phy of fast-ion dynamics. Nucl. Fusion, 56:106024, 2016

    2016

  52. [60]

    Weiland et al

    M. Weiland et al. Enhancement of the FIDA diagnostic at ASDEX Upgrade for velocity-space tomography. Plasma Phys. Control. Fusion, 58:025012, 2016

    2016

  53. [61]

    Weiland et al

    M. Weiland et al. Phase-space resolved measurement of 2nd harmonic ion cyclotron heating using fida tomography at the asdex upgrade tokamak. Nucl. Fusion , 57:116058, 2017

    2017

  54. [62]

    A. S. Jacobsen et al. Benchmark and combined velocity- space tomography of fast-ion D-Alpha spectroscopy and collective Thomson scattering measurements. Plasma Phys. Control. Fusion, 58:042002, 2016

    2016

  55. [63]

    A. S. Jacobsen et al. Inversion methods for fast-ion velocity-space tomography in fusion plasmas. Plasma Phys. Control. Fusion, 58:045016, 2016

    2016

  56. [64]

    Rasmussen et al

    J. Rasmussen et al. Collective Thomson scattering measurements of fast-ion transport due to sawtooth crashes in ASDEX Upgrade. Nucl. Fusion , 56:112014, 2016

    2016

  57. [65]

    Madsen et al

    B. Madsen et al. Velocity-space tomography using prior information at MAST. Rev. Sci. Instrum. , 89:10D125, 2018

    2018

  58. [66]

    Madsen et al

    B. Madsen et al. Fast-ion velocity-space tomography using slowing-down regularization in EAST plasmas with co- and counter-current neutral beam injection. Plasma Phys. Control. Fusion, 62:115019, 2020

    2020

  59. [67]

    Su et al

    J. Su et al. Reconstructions of velocity distributions from fast-ion D-alpha (FIDA) measurements on EAST. Plasma Sci. Tech., 23:at press, 2021

    2021

  60. [68]

    Geiger et al

    B. Geiger et al. Study of the fast-ion distribution function in the tcv tokamak based on fida spectroscopy and the transp code. Plasma Phys. Control. Fusion , 59:115002, 2017

    2017

  61. [69]

    Salewski et al

    M. Salewski et al. Alpha-particle velocity-space diagnostic in ITER. Nucl. Fusion, 58:096019, 2018

    2018

  62. [70]

    Salewski et al

    M. Salewski et al. Deuterium temperature, drift velocity, and density measurements in non-Maxwellian plasmas at ASDEX Upgrade. Nucl. Fusion, 58:036017, 2018

    2018

  63. [71]

    Salewski

    M. Salewski. Fast-ion diagnostic in fusion plasmas by velocity-space tomography. Dr. techn. thesis, Technical University of Denmark , 2019

    2019

  64. [72]

    Heidbrink et al

    W.W. Heidbrink et al. Measurements of fast-ion acceleration at cyclotron harmonics using Balmer- alpha spectroscopy. Plasma Phys. Control. Fusion , 49(9):1457–1475, September 2007

    2007

  65. [73]

    Salewski et al

    M. Salewski et al. On velocity-space sensitivity of fast-ion D-alpha spectroscopy. Plasma Phys. Control. Fusion , 56(10):105005, October 2014

    2014

  66. [74]

    Salewski et al

    M. Salewski et al. On velocity space interrogation regions of fast-ion collective Thomson scattering at ITER. Nucl. Integrated Data Analysis and Validation 21 Fusion, 51(8):083014, 2011

    2011

  67. [75]

    A. S. Jacobsen et al. Velocity-space sensitivity of neutron spectrometry measurements. Nucl. Fusion , 55(5):053013, May 2015

    2015

  68. [76]

    A. S. Jacobsen et al. Velocity-space sensitivities of neutron emission spectrometers at JET and ASDEX Upgrade. Rev. Sci. Instrum. , 88:073506, 2017

    2017

  69. [77]

    Salewski et al

    M. Salewski et al. Velocity-space observation regions of high-resolution two-step reaction gamma-ray spec- troscopy. Nucl. Fusion, 55(9):093029, 2015

    2015

  70. [78]

    Salewski et al

    M. Salewski et al. Fast-ion energy resolution by one- step reaction gamma-ray spectrometry. Nucl. Fusion , 56:046009, 2016

    2016

  71. [79]

    Galdon-Quiroga et al

    J. Galdon-Quiroga et al. Velocity-space sensitivity and tomography of scintillator-based fast-ion loss detectors. Plasma Phys. Control. Fusion , 60:105005, 2018

    2018

  72. [80]

    Heidbrink et al

    W.W. Heidbrink et al. Phase-space sensitivity (weight functions) of 3 MeV proton diagnostics. Plasma Phys. Control. Fusion, 63:055008, 2021

    2021

  73. [81]

    Schmidt et al

    B.S. Schmidt et al. Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks. Rev. Sci. Instrum , 92:at press, 2021

    2021

  74. [82]

    Heidbrink

    W.W. Heidbrink. Fast-ion D α measurements of the fast-ion distribution (invited). Rev. Sci. Instrum. , 81(10):10D727, October 2010

    2010

  75. [83]

    Nocente et al

    M. Nocente et al. MeV range particle physics studies in tokamak plasmas using gamma-ray spectroscopy. Plasma Phys. Control. Fusion , 62:014015, 2020

    2020

  76. [84]

    Salewski et al

    M. Salewski et al. Comparison of fast ion collective Thomson scattering measurements at ASDEX Upgrade with numerical simulations. Nucl. Fusion, 50(3):035012, March 2010

    2010

  77. [85]

    Lawson and R.J

    C.L. Lawson and R.J. Hanson. Solving Least Squares Problems. Prentic-Hall, Englewood Cliffs, NJ, 1974

    1974

  78. [86]

    Salewski et al

    M. Salewski et al. Diagnostic of fast-ion energy spectra and densities in magnetized plasmas. J. Instrum., submitted

  79. [87]

    Donn´ e et al

    A.J.H. Donn´ e et al. Progress in the ITER Physics Basis. Chapter 7: Diagnostics. Nucl. Fusion, 47(6):S337–S384, June 2007

    2007

  80. [88]

    Stagner and W.W

    L. Stagner and W.W. Heidbrink. Action-angle formulation of generalized, orbit-based, fast-ion diagnostic weight functions. Phys. Plasmas , 24:092505, 2017

    2017

  81. [89]

    J¨ arleblad et al

    H. J¨ arleblad et al. Fast-ion orbit sensitivity of neutron emission spectroscopy diagnostics. Rev. Sci. Instrum , 92:043526, 2021

    2021

  82. [90]

    Stagner, W.W

    L. Stagner, W.W. Heidbrink, M. Salewski, A. Schou Jacobsen, and Benedikt Geiger. Orbit Tomography of energetic particle distribution functions. Nucl. Fusion ,

  83. [2022]

    at press, DOI: https://doi.org/10.1088/1741- 4326/ac3ed2

    doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.