Language：English   Publishing type：Research paper (scientific journal)   Publisher：Review of Scientific Instruments  

Accurate characterization of thermal diffusion and blackbody radiation on the detector foil is crucial in infrared imaging video bolometry (IRVB) for reliably inferring the spatial distribution of plasma radiation. This paper presents a new inference framework for modeling blackbody radiation and thermal diffusion power densities using Gaussian process regression. This method is validated with both synthetic and experimental IRVB data, producing reliable results without the need for temporal or spatial averaging. In addition, the effects of noise level and foil material are analyzed, and both the limitations of this framework and strategies for improving its performance are identified.

DOI： 10.1063/5.0313633

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

Reliable estimation of equilibrium flow shear from reciprocating probe measurements is challenging since the quantity of interest corresponds to the second derivative of the observable plasma potential. In addition, a time series of the plasma potential obtained by plunging a probe is affected by both the probe head position and plasma fluctuations, complicating the estimation of equilibrium components and their errors. We tackle this problem by employing Gaussian process regression that is able to infer even the derivatives of a spatial or temporal profile in the form of a probability distribution function. The proposed inference framework is validated by using synthetic data generated by gyrofluid simulations. While the inference result based on a single plunge is unstable in certain spatial locations, we have obtained reasonable agreement between the inference result and the true flow shear profile by combining data sets taken from several plunges.

DOI： 10.1063/5.0254473

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

Reliable equilibrium reconstruction is indispensable for understanding and controlling hot magnetized plasmas to achieve fusion reactors. In axisymmetric systems, current and pressure profiles that satisfy the force balance conditions are given by the Grad-Shafranov (GS) equation. While many novel approaches have been developed to swiftly and robustly find an optimum solution of the GS equation, approaches based on a single solution search may not be adaptable if diagnostics fail to provide sufficient constraints. Here, we investigate the solution space of the GS equation when only basic edge magnetic measurements are available. By combining Gaussian process regression and Markov chain Monte Carlo sampling within the Bayesian framework, we treat each current element as an independent variable and evaluate the probability distribution that describes all possible solutions. We have applied this inference frame to the geometry of the PLATO tokamak and shown that the flux surface locations can be determined relatively well only from 16 pick-up coils, 4 flux loops and a diamagnetic loop. On the other hand, the toroidal current density is inferred with limited success, and the inferences of the safety factor and pressure profiles are difficult. The characterization of possible choices of equilibria realized by this inference frame will help optimize diagnostic setups for equilibrium reconstruction.

DOI： 10.1088/1361-6587/ad9521

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Other Link： https://iopscience.iop.org/article/10.1088/1361-6587/ad9521/pdf

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-024-64380-2

Event date： 2026.3

Language：English   Presentation type：Oral presentation (general)  

Venue：東京   Country：Japan  

Event date： 2025.12

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Event date： 2025.11

Language：Japanese   Presentation type：Poster presentation  

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Event date： 2025.9

Language：English   Presentation type：Oral presentation (invited, special)  

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Other Link： https://tokuda-lab.org/post-1496/

Event date： 2025.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Other Link： https://tokuda-lab.org/post-1479/

Type：Academic society, research group, etc. 

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Type：Academic society, research group, etc. 

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Type：Academic society, research group, etc. 

Number of participants：100

計測技術とデータ解析の高度な融合により、それら単体では成し得ないブレイククスルーを目指す「計測インフォマティクス」と呼ぶべき研究領域が近年勃興しています。一方、データ解析の発展は数学や統計学が与える原理や理論保証に支えられています。逆にこうした実問題から得た着想が数学や統計学を発展させる契機と成り得ることも歴史が示しています。本セミナーでは物質科学を始めとした計測・解析分野と数学・統計学の研究者が互いのシーズとニーズを共有し、相互発展の可能性を探ります。

Type：Academic society, research group, etc. 

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Type：Academic society, research group, etc. 

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Audience：High school students

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Audience：College students,　Graduate students,　Researchesrs,　Scientific,　Company,　Governmental agency

Type：Seminar, workshop

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Audience：College students,　Graduate students,　Researchesrs,　Scientific,　Company,　Governmental agency

Type：Seminar, workshop

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