MS10DSセミナー：Sadruddin Benkadda「Machine Learning Based Tungsten Spectroscopy in WEST Tokamak」

Friday Jan 30, 2026, 10:30 AM → 11:30 AM Asia/Tokyo

Sadruddin Benkadda氏（CNRS)によるセミナーです。

• ハイブリッド開催（核融合研研究I期棟501号室 / Zoom）
• 対面参加・遠隔参加のどちらででも、下記「参加申し込み」に登録してください。
• 参加申し込み：https://us06web.zoom.us/meeting/register/yI7A5W3pT0GWIC8NpK9h9A

 

主催：ムーンショット目標10プロジェクト「超次元状態エンジニアリングによる未来予測型デジタルシステム」（https://ms10ds.nifs.ac.jp/）

主催：NIFSプラズマ量子プロセスユニット

本セミナーの公式URL：https://indico.nifs.ac.jp/e/ms10ds-semi-20260130

 

10:30 AM → 11:30 AM MS10DSセミナー「Machine Learning Based Tungsten Spectroscopy in WEST Tokamak」

Machine Learning Based Tungsten Spectroscopy in WEST Tokamak
Sadruddin Benkadda, PIIM Laboratory, Aix Marseille University–CNRS

Abstract
A machine-learning approach to tungsten spectroscopy analysis is developed using measurements from WEST tokamak plasmas. A Random Forest model is trained to predict the maximum electron temperature, denoted as Te max, from tungsten brightness spectra in the wavelength range 45–65 angstroms, measured along a mobile line of sight. The model achieves prediction errors typically below 5 percent over a broad electron temperature range from 0.5 to 4 kilo–electronvolts. Feature-importance analysis identifies both physically meaningful wavelengths and others that, although lacking direct physical interpretability, contribute to improved prediction stability and accuracy. In addition, principal component analysis is performed to investigate the relationship between spectral variance and Te max, revealing that previously unaccounted-for parameters influence the spectral shape. When combined with the wavelength-importance analysis, these results may help inform atomic structure and collisional–radiative models, leading to an improved understanding of tungsten spectral emission.