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Comparison of Tomographic Reconstruction Algorithms for Infrared Imaging Video Bolometer Diagnostic in Plasma Devices

T0 review · 1 major / 1 minor · reviewed 2026-05-19 · grok-4.3

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Pith's one-line read Three tomographic algorithms for IRVB plasma diagnostics trade reconstruction accuracy for numerical stability and real-time suitability.

desk verdict This paper compares three existing tomographic algorithms on synthetic IRVB data and maps their practical tradeoffs, but stays within simulation bounds. read the letter →

arxiv 2605.17459 v1 pith:SQOYEKM7 submitted 2026-05-17 physics.plasm-ph physics.ins-det

Vinit Pandya , Santosh P. Pandya , Ansh Patel , Kumudni Tahiliani , Kumar Ajay This is my paper · ORCID
classification physics.plasm-phphysics.ins-det
keywords infraredimagingvideobolometertomographicreconstructionplasmaradiationemissivityminimumfisherinformationphillips-tikhonovregularizationmaximumlikelihoodexpectationmaximizationdiagnostics
checked against Cost.FunctionalEquationalexander_duality_circle_linking
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 compares Minimum Fisher Information, Phillips-Tikhonov regularization, and Maximum-Likelihood Expectation-Maximization methods for inverting line-integrated IRVB signals into 2D plasma radiation emissivity profiles on a poloidal cross-section. Tests use synthetic phantoms for symmetric Gaussian, hollow, asymmetric, and divertor-side distributions, examining effects of viewing geometry, noise, non-negativity, prior assumptions, convergence speed, and peak preservation. A sympathetic reader would care because reliable emissivity maps reveal where plasma loses energy through radiation, directly informing fusion device performance and control. The work shows each method has distinct strengths depending on whether the goal is high accuracy offline or fast processing during experiments.

What carries the argument

Systematic comparison of MFI, PTR, and MLEM tomographic inversion algorithms on synthetic IRVB brightness data, assessing performance across geometry, noise robustness, and computational demands.

What would settle it

Reconstruction of emissivity profiles from actual experimental IRVB data on a plasma device, followed by cross-validation against independent radiation measurements or other diagnostics.

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Extended reading notes

Core claim

Through forward modeling of IRVB pinhole camera signals and application to representative emissivity phantoms, the study finds that MFI balances accuracy and robustness, PTR provides stable results sensitive to regularization parameters, and MLEM handles non-negativity and noise well but requires more iterations for convergence, leading to practical recommendations for choosing among them based on IRVB camera configuration and usage mode.

Load-bearing premise

The synthetic phantoms and forward modeling process accurately represent the line-integrated signals and noise characteristics encountered in real IRVB measurements on plasma devices.

Editorial extensions

If this is right

  • Choice of reconstruction method determines whether IRVB data can support real-time plasma monitoring or must be processed offline.
  • Non-negativity constraints and noise levels in bolometer signals favor MLEM for certain asymmetric radiation profiles.
  • Viewing geometry configurations in the pinhole camera affect the sensitivity of each algorithm to prior assumptions.
  • Peak preservation in reconstructed emissivity distributions improves understanding of localized radiation losses near the divertor.

Reading between the lines

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

  • These tradeoffs could guide algorithm selection in similar 2D radiation tomography setups on other fusion devices if the noise models transfer.
  • Combining elements from different methods, such as using MLEM outputs to inform MFI priors, might yield hybrid approaches with better overall performance.
  • Validation on real data would likely expose additional challenges from calibration errors or foil response variations not present in synthetics.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 1 minor

Summary. The manuscript compares three tomographic reconstruction methods—Minimum Fisher Information (MFI), Phillips-Tikhonov regularization (PTR), and Maximum-Likelihood Expectation-Maximization (MLEM)—for inverting line-integrated signals from Infrared Imaging Video Bolometer (IRVB) pinhole-camera measurements to recover 2D plasma emissivity distributions. Synthetic phantoms are generated for four representative profiles (centered Gaussian, hollow, asymmetric, and divertor-side) via a forward model; the algorithms are assessed on viewing geometry, non-negativity, noise robustness, prior sensitivity, convergence speed, and peak preservation, with the goal of identifying practical tradeoffs for real-time versus offline use.

Significance. If the synthetic forward model and phantoms adequately capture the dominant noise and geometric effects present in actual IRVB data, the comparison supplies actionable guidance for selecting reconstruction algorithms in fusion-plasma radiation diagnostics, potentially improving the fidelity of 2-D emissivity maps used for power-balance studies.

major comments (1)
  1. IRVB forward modelling process and synthetic phantoms section: the forward model assumes ideal pinhole projection, uniform foil response, and additive noise whose statistics are not shown to reproduce measured IRVB foil thermal noise or line-of-sight integration through 3-D toroidal structure. Because the reported accuracy, stability, and convergence rankings rest directly on these synthetic benchmarks, the absence of explicit validation against experimental IRVB signals undermines the transferability of the practical tradeoffs to real plasma-device data.
minor comments (1)
  1. Abstract: the final sentence could be expanded to state the principal ranking or recommendation that emerges from the comparison rather than only listing the evaluation criteria.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript comparing tomographic reconstruction algorithms for IRVB diagnostics. We address the major comment below and have made revisions to clarify the scope and limitations of our synthetic study.

read point-by-point responses

  1. Referee: IRVB forward modelling process and synthetic phantoms section: the forward model assumes ideal pinhole projection, uniform foil response, and additive noise whose statistics are not shown to reproduce measured IRVB foil thermal noise or line-of-sight integration through 3-D toroidal structure. Because the reported accuracy, stability, and convergence rankings rest directly on these synthetic benchmarks, the absence of explicit validation against experimental IRVB signals undermines the transferability of the practical tradeoffs to real plasma-device data.

    Authors: We agree that the forward model employs idealized assumptions, including perfect pinhole projection, uniform foil response, and simplified additive noise, without direct reproduction of measured IRVB thermal noise statistics or full 3-D toroidal line-of-sight effects. The manuscript's primary aim is a controlled, side-by-side comparison of MFI, PTR, and MLEM under representative synthetic conditions to isolate algorithmic tradeoffs in accuracy, non-negativity, noise robustness, prior sensitivity, convergence, and peak preservation. Such synthetic benchmarking is a standard first step in diagnostic algorithm development. To address the referee's valid concern about transferability, we have revised the manuscript by expanding the discussion section to explicitly state these modeling assumptions and their potential impact on real-data performance, and by adding a forward-looking statement on the value of future experimental validation with actual IRVB measurements from plasma devices. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity in empirical comparison of reconstruction methods

full rationale

The paper is an empirical benchmark study comparing three standard tomographic algorithms (MFI, PTR, MLEM) on synthetic emissivity phantoms generated by a forward model. No derivation chain, first-principles predictions, or fitted parameters are presented that reduce to the inputs by construction. Evaluation metrics (accuracy, stability, convergence) are applied to independent synthetic cases without self-referential fitting or load-bearing self-citations. The work is self-contained as a practical tradeoff analysis.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central comparison rests on the assumption that synthetic phantoms capture the essential features of real IRVB signals; no free parameters or invented entities are explicitly introduced in the abstract.

assumptions (1)
  • domain assumption Synthetic phantoms with Gaussian, hollow, asymmetric, and divertor profiles represent typical plasma emissivity distributions.
    Used as the basis for validating reconstruction performance.

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Cite this review

Pith. "Pith review of Comparison of Tomographic Reconstruction Algorithms for Infrared Imaging Video Bolometer Diagnostic in Plasma Devices." pith.science (2026). https://pith.science/paper/SQOYEKM7

@misc{pith2026260517459,
  author       = {Pith},
  title        = {Pith review of: Comparison of Tomographic Reconstruction Algorithms for Infrared Imaging Video Bolometer Diagnostic in Plasma Devices},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SQOYEKM7}},
  note         = {Machine review of arXiv:2605.17459}
}
read the original abstract

Infrared Imaging Video Bolometer (IRVB) measures total radiation power loss from plasma in 2 dimensions through a pinhole camera geometry. Where a free-standing thin metal foil act as a broad band absorber from Soft X-Rays to IR radiation. This configuration produces line-integrated signals with poloidal and toroidal coverage that must be inverted to recover the plasma radiation emissivity distribution on a poloidal cross-section. This study compares the tomographic methods implemented to IRVB brightness data reconstruction, namely Minimum Fisher Information (MFI), Phillips-Tikhonov regularization (PTR), and Maximum-Likelihood Expectation-Maximization (MLEM). The comparison assessment is organized around several aspects of bolometer measurements, namely viewing geometry configuration, non-negativity, robustness to noise, sensitivity to prior assumptions, convergence speed, and peak preservation. The present work also details the IRVB forward modelling process, construction of synthetic phantoms, and a validation of these reconstruction methods based on typical expected emissivity profiles, namely symmetric Gaussian distribution at plasma center, symmetric hollow-radiation emissivity profile, asymmetric radiation profiles across the poloidal cross-section, and divertor-side radiation emission profiles. The outcome is to emphasize the practical tradeoffs among reconstruction accuracy, numerical stability, and suitability for real-time or offline usage of these reconstruction methods, particularly for the IRVB camera viewing system.

Figures

Figures reproduced from arXiv: 2605.17459 by the authors.

Figure 5
Figure 5. (a) Core Gaussian emissivity phantom in the R– [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. (a) Hollow Gaussian (annular ring) emissivity phantom. (b) [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figure 7
Figure 7. (a) Low-field-side asymmetric emissivity phantom. (b) Foil brightness showing the characteristic asymmetric distribution. D. Double-Null Divertor Profile Two Gaussian blobs replicate emission at divertor strike points in a double-null configuration: 𝜖4(𝑅, 𝑍) = ∑ 𝜀ₖ 𝑒𝑥𝑝 [− (𝑅−𝑅ₖ) 2+(𝑍−𝑍ₖ) 2 2𝜎ₖ 2 ] 2 𝑘=1 (9) Upper blob: (𝑅₁, 𝑍₁) = (−15,+18) 𝑐𝑚, 𝜎₁ = 4 𝑐𝑚, 𝜀₁ = 2.0. Lower blob: (𝑅₂, 𝑍₂) = (−15,−18) 𝑐𝑚, 𝜎₂ = 4 𝑐𝑚, 𝜀₂ =… view at source ↗
Figures from the paper (3 more)
Figure 8
Figure 8. Figure 8: (a) Double-null divertor emissivity phantom (two Gaussian blobs). (b) Corresponding foil brightness showing two vertically separated peaks. E. Impurity Emission Profile The electron temperature and density profiles corresponding to synthetic plasma profiles in a medium…
Figure 6
Figure 6. Figure 6: Tomographic reconstructions for all phantoms using MFI, PTR [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 9
Figure 9. Figure 9: PTR-1, PTR-2, and MLEM all remain below 0.75 s across the entire study, as their per-reconstruction cost scales primarily with the fixed grid dimension (625 unknowns) rather than the number of measurements. MFI rises from approximately 2.0 s at N = 4 to 3.8 s at N = 20…

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Works this paper leans on

42 extracted references · 42 canonical work pages

  1. [1]

    A low noise highly integrated bolometer array for absolute measurement of VUV and soft x radiation,

    K. F. Mast, J. C. Vallet, C. Andelfinger, P. Betzler, H. Kraus, and G. Schramm, “A low noise highly integrated bolometer array for absolute measurement of VUV and soft x radiation,” Review of Scientific Instruments , vol. 62, no. 3, pp. 744 –750, Mar. 1991, doi: 10.1063/1.1142078

    work page doi:10.1063/1.1142078 1991

  2. [2]

    A new metal resistor bolometer for measuring vacuum ultraviolet and soft x radiation,

    E. R. Müller and F. Mast, “A new metal resistor bolometer for measuring vacuum ultraviolet and soft x radiation,” Journal of Applied Physics, vol. 55, no. 7, pp. 2635–2641, Apr. 1984, doi: 10.1063/1.333272

    work page doi:10.1063/1.333272 1984

  3. [3]

    Bolometric diagnostics in JET,

    K. F. Mast, H. Krause, K. Behringer, A. Bulliard, and G. Magyar, “Bolometric diagnostics in JET,” Review of Scientific Instruments, vol. 56, no. 5, pp. 969 –971, May 1985, doi: 10.1063/1.1138007

    work page doi:10.1063/1.1138007 1985

  4. [4]

    Application of AXUV diode detectors at ASDEX Upgrade,

    M. Bernert et al., “Application of AXUV diode detectors at ASDEX Upgrade,” Review of Scientific Instruments , vol. 85, no. 3, p. 033503, Mar. 2014, doi: 10.1063/1.4867662

    work page doi:10.1063/1.4867662 2014

  5. [6]

    Infrared imaging video bolometer,

    B. J. Peterson, “Infrared imaging video bolometer,” Review of Scientific Instruments , vol. 71, no. 10, pp. 3696–3701, Oct. 2000, doi: 10.1063/1.1290044

    work page doi:10.1063/1.1290044

  6. [7]

    Development of Infrared Imaging Video Bolometer for the ADITYA Tokamak,

    S. P. Pandya, S. N. Pandya, Z. Shaikh, S. Shaikh, J. Govindarajan, and Aditya Team, “Development of Infrared Imaging Video Bolometer for the ADITYA Tokamak,” Plasma and Fusion Research , vol. 7, no. 0, pp. 2402089 –2402089, 2012, doi: 10.1585/pfr.7.2402089

    work page doi:10.1585/pfr.7.2402089 2012

  7. [8]

    Consideration of signal to noise ratio for an imaging bolometer for ITER,

    B. J. Peterson, R. Reichle, S. Pandya, M. G. O’Mullane, and K. Mukai, “Consideration of signal to noise ratio for an imaging bolometer for ITER,” Review of Scientific Instruments, vol. 92, no. 4, p. 043534, Apr. 2021, doi: 10.1063/5.0043201

    work page doi:10.1063/5.0043201 2021

  8. [9]

    Three-dimensional tomographic imaging for dynamic radiation behavior study using infrared imaging video bolometers in large helical device plasma,

    R. Sano et al., “Three-dimensional tomographic imaging for dynamic radiation behavior study using infrared imaging video bolometers in large helical device plasma,” Review of Scientific Instruments, vol. 87, no. 5, p. 053502, May 2016, doi: 10.1063/1.4948392

    work page doi:10.1063/1.4948392 2016

Show all 42 references
  1. [10]

    Bolometric technics on TFR 600,

    “Bolometric technics on TFR 600,” Journal of Nuclear Materials, vol. 93 –94, pp. 377 –382, Oct. 1980, doi: 10.1016/0022-3115(80)90351-7

    1980 doi

  2. [11]

    Infrared calorimeter for time-resolved plasma energy flux measurement,

    J. C. Ingraham and G. Miller, “Infrared calorimeter for time-resolved plasma energy flux measurement,” Review of Scientific Instruments, vol. 54, no. 6, pp. 673–676, Jun. 1983, doi: 10.1063/1.1137451

    1983 doi

  3. [12]

    Development of plasma bolometers using fiber -optic temperature sensors,

    M. L. Reinke et al., “Development of plasma bolometers using fiber -optic temperature sensors,” Review of Scientific Instruments, vol. 87, no. 11, p. 11E708, Nov. 2016, doi: 10.1063/1.4960421

    2016 doi

  4. [13]

    First demonstration of a fiber optic bolometer on a tokamak plasma (invited),

    S. Lee et al. , “First demonstration of a fiber optic bolometer on a tokamak plasma (invited),” Review of Scientific Instruments , vol. 93, no. 12, p. 123515, Dec. 2022, doi: 10.1063/5.0099546

    2022 doi

  5. [14]

    Estimates of foil thickness, signal, noise, and nuclear heating of imaging bolometers for ITER,

    B. J. Peterson, T. Nishitani, R. Reichle, K. Munechika, M. G. O’Mullane, and K. Mukai, “Estimates of foil thickness, signal, noise, and nuclear heating of imaging bolometers for ITER,” J. Inst., vol. 17, no. 06, p. P06034, Jun. 2022, doi: 10.1088/1748-0221/17/06/P06034

    2022 doi

  6. [15]

    Pandya et al

    S. Pandya et al. , Tangential viewing Infrared Imaging Video Bolometer developed for the ADITYA tokamak and its comparison with 2-D plasma power loss model. 2013

    2013

  7. [16]

    Pandya et al., First results from the Infrared Imaging Video Bolometer in SST-1 Tokamak

    S. Pandya et al., First results from the Infrared Imaging Video Bolometer in SST-1 Tokamak. 2014

    2014

  8. [17]

    Introducing minimum Fisher regularisation tomography to AXUV and soft x -ray diagnostic systems of the COMPASS tokamak,

    J. Mlynar et al. , “Introducing minimum Fisher regularisation tomography to AXUV and soft x -ray diagnostic systems of the COMPASS tokamak,” Review of Scientific Instruments, vol. 83, no. 10, p. 10E531, Oct. 2012, doi: 10.1063/1.4738648

    2012 doi

  9. [18]

    Bolometer tomography on Wendelstein 7-X for study of radiation asymmetry,

    D. Zhang et al., “Bolometer tomography on Wendelstein 7-X for study of radiation asymmetry,” Nucl. Fusion, vol. 61, no. 11, p. 116043, Nov. 2021, doi: 10.1088/1741 - 4326/ac2778

    2021 doi

  10. [19]

    Infrared Imaging Bolometer for the HL-2A Tokamak,

    J. Gao et al., “Infrared Imaging Bolometer for the HL-2A Tokamak,” Plasma Sci. Technol., vol. 18, no. 6, pp. 590– 594, Jun. 2016, doi: 10.1088/1009-0630/18/6/02

    2016 doi

  11. [20]

    Inversion of infrared imaging bolometer based on one -dimensional and three - dimensional modeling in HL -2A,

    J. M. Gao et al. , “Inversion of infrared imaging bolometer based on one -dimensional and three - dimensional modeling in HL -2A,” Review of Scientific Instruments, vol. 85, no. 4, p. 043505, Apr. 2014, doi: 10.1063/1.4870408

    2014 doi

  12. [21]

    Minimum Fisher regularization of image reconstruction for infrared imaging bolometer on HL-2A,

    J. M. Gao et al. , “Minimum Fisher regularization of image reconstruction for infrared imaging bolometer on HL-2A,” Review of Scientific Instruments, vol. 84, no. 9, p. 093503, Sep. 2013, doi: 10.1063/1.4820920

    2013 doi

  13. [22]

    Infrared imaging Video Bolometer (IRVB) system for KSTAR

    D. Seo, B. J. Peterson, K. Mukai, and R. Sano, “Infrared imaging Video Bolometer (IRVB) system for KSTAR”

  14. [23]

    Progress of infra -red imaging video bolometer of KSTAR

    S. Oh, J. Jang, B. Peterson, W. Choe, and S. -H. Hong, “Progress of infra -red imaging video bolometer of KSTAR”

  15. [24]

    High -fidelity tomographic reconstruction for infrared video bolometers through physics -based background radiation modeling,

    Y. S. Han, S. Oh, and W. Choe, “High -fidelity tomographic reconstruction for infrared video bolometers through physics -based background radiation modeling,” Review of Scientific Instruments, vol. 97, no. 4, p. 043503, Apr. 2026, doi: 10.1063/5.0320670

    2026 doi

  16. [25]

    Tomographic reconstruction of two - dimensional radiated power distribution during impurity injection in KSTAR plasmas using an infrared imaging video bolometer,

    J. Jang et al. , “Tomographic reconstruction of two - dimensional radiated power distribution during impurity injection in KSTAR plasmas using an infrared imaging video bolometer,” Current Applied Physics, vol. 18, no. 4, pp. 461 –468, Apr. 2018, doi: 10.1016/j.cap.2018.01.009

    2018 doi

  17. [26]

    Radiation profile reconstruction of infrared imaging video bolometer data using a machine learning algorithm,

    S. Oh, J. Jang, and B. Peterson, “Radiation profile reconstruction of infrared imaging video bolometer data using a machine learning algorithm,” Plasma Phys. Control. Fusion , vol. 62, no. 3, p. 035014, Mar. 2020, doi: 10.1088/1361-6587/ab6b4b

    2020 doi

  18. [27]

    Reconstruction algorithm for the runaway electron energy distribution function of the ITER hard x -ray monitor,

    A. Patel et al. , “Reconstruction algorithm for the runaway electron energy distribution function of the ITER hard x -ray monitor,” Phys. Scr., vol. 98, no. 8, p. 085604, Aug. 2023, doi: 10.1088/1402-4896/ace135

    2023 doi

  19. [28]

    Correction of JET bolometric maximum likelihood tomography for local gas puffing,

    E. Peluso et al., “Correction of JET bolometric maximum likelihood tomography for local gas puffing,” Plasma 11 Phys. Control. Fusion , vol. 65, no. 7, p. 075003, Jul. 2023, doi: 10.1088/1361-6587/accd1c

    2023 doi

  20. [29]

    Application of Natural Basis Functions to Soft X-ray Tomography

    L. C. Ingesson, “Application of Natural Basis Functions to Soft X-ray Tomography”

  21. [30]

    X -ray tomography on the TCV tokamak,

    M. Anton et al. , “X -ray tomography on the TCV tokamak,” Plasma Physics and Controlled Fusion , vol. 38, no. 11, p. 1849, Nov. 1996, doi: 10.1088/0741 - 3335/38/11/001

    1996 doi

  22. [31]

    Plasma production and preliminary results from the ADITYA Upgrade tokamak,

    R. L. Tanna et al., “Plasma production and preliminary results from the ADITYA Upgrade tokamak,” Plasma Sci. Technol. , vol. 20, no. 7, p. 074002, Jul. 2018, doi: 10.1088/2058-6272/aabb4f

    2018 doi

  23. [32]

    Overview of operation and experiments in the ADITYA-U tokamak,

    R. L. Tanna et al. , “Overview of operation and experiments in the ADITYA-U tokamak,” Nucl. Fusion, vol. 59, no. 11, p. 112006, Nov. 2019, doi: 10.1088/1741- 4326/ab0a9e

    2019 doi

  24. [33]

    Stabilization of sawtooth instability by short gas pulse injection in ADITYA-U tokamak,

    S. Dolui et al., “Stabilization of sawtooth instability by short gas pulse injection in ADITYA-U tokamak,” Phys. Rev. Research, vol. 7, no. 3, p. 033161, Aug. 2025, doi: 10.1103/wbkn-kz71

    2025 doi

  25. [34]

    Investigation of atomic and molecular processes in H α emission through modelling of measured H α emissivity profile using DEGAS2 in the ADITYA tokamak,

    R. Dey et al. , “Investigation of atomic and molecular processes in H α emission through modelling of measured H α emissivity profile using DEGAS2 in the ADITYA tokamak,” Nucl. Fusion, vol. 59, no. 7, p. 076005, Jul. 2019, doi: 10.1088/1741-4326/ab0f01

    2019 doi

  26. [35]

    Modeling of the Hα Emission from ADITYA Tokamak Plasmas,

    R. Dey et al. , “Modeling of the Hα Emission from ADITYA Tokamak Plasmas,” Atoms, vol. 7, no. 4, p. 95, Oct. 2019, doi: 10.3390/atoms7040095

    2019 doi

  27. [36]

    Predictive analysis of EC wave propagation and absorption in the ADITYA-U tokamak,

    J. Kumar et al. , “Predictive analysis of EC wave propagation and absorption in the ADITYA-U tokamak,” Physics of Plasmas , vol. 32, no. 11, p. 112504, Nov. 2025, doi: 10.1063/5.0289391

    2025 doi

  28. [37]

    Abel inversion of asymmetric plasma density profile at Aditya tokamak,

    N. Y. Joshi, P. K. Atrey, and S. K. Pathak, “Abel inversion of asymmetric plasma density profile at Aditya tokamak,” J. Phys.: Conf. Ser., vol. 208, p. 012129, Feb. 2010, doi: 10.1088/1742-6596/208/1/012129

    2010 doi

  29. [38]

    FLYCHK: Generalized population kinetics and spectral model for rapid spectroscopic analysis for all elements,

    H.-K. Chung, M. H. Chen, W. L. Morgan, Y. Ralchenko, and R. W. Lee, “FLYCHK: Generalized population kinetics and spectral model for rapid spectroscopic analysis for all elements,” High Energy Density Physics, vol. 1, no. 1, pp. 3 –12, Dec. 2005, doi: 10.1016/j.hedp.2005.07.001

    2005 doi

  30. [39]

    Study of iron impurity behaviour in the ADITYA tokamak,

    S. Patel et al., “Study of iron impurity behaviour in the ADITYA tokamak,” Nucl. Fusion , vol. 59, no. 8, p. 086019, Aug. 2019, doi: 10.1088/1741-4326/ab1f12

    2019 doi

  31. [40]

    Investigation of oxygen impurity transport using the O 4+ visible spectral line in the Aditya tokamak,

    M. B. Chowdhuri et al. , “Investigation of oxygen impurity transport using the O 4+ visible spectral line in the Aditya tokamak,” Nucl. Fusion , vol. 53, no. 2, p. 023006, Feb. 2013, doi: 10.1088/0029 - 5515/53/2/023006

    2013 doi

  32. [41]

    Physics studies of ADITYA & ADITYA-U tokamak plasmas using spectroscopic diagnostics,

    R. Manchanda et al. , “Physics studies of ADITYA & ADITYA-U tokamak plasmas using spectroscopic diagnostics,” Nucl. Fusion, vol. 62, no. 4, p. 042014, Apr. 2022, doi: 10.1088/1741-4326/ac2cf6

    2022 doi

  33. [42]

    The Use of the L-Curve in the Regularization of Discrete Ill -Posed Problems,

    P. C. Hansen and D. P. O’Leary, “The Use of the L-Curve in the Regularization of Discrete Ill -Posed Problems,” SIAM J. Sci. Comput. , vol. 14, no. 6, pp. 1487 –1503, Nov. 1993, doi: 10.1137/0914086

    1993 doi

  34. [43]

    Modern numerical methods for plasma tomography optimisation,

    M. Odstrcil, J. Mlynar, T. Odstrcil, B. Alper, and A. Murari, “Modern numerical methods for plasma tomography optimisation,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 686, pp. 156–161, S...

    2012 doi

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