Diagnostic Dual Frugal for Pulsed Magnetized Confinement Platforms: Villari/hBN Sensing, Low-Cost Brillouin/Raman Backscatter and Inter-Shot Machine Learning
Year: 2026
Type: preprint
Abstract: Description This work proposes a frugal dual diagnostic architecture for pulsed magnetized confinement and magnetized target fusion platforms, designed for independent laboratories, educational facilities and low-budget experimental environments. The concept combines two complementary diagnostic channels: A Villari/hBN sensor, previously proposed for simultaneous magnetic-field and mechanical-stress monitoring through VB spin centers in hexagonal boron nitride. A low-cost Brillouin/Raman stimulated backscatter channel, using an optical pick-off, dichroic or interference filters and fast photodiodes to provide coarse discrimination between SBS and SRS signals. An optional second-hand Fabry-Pérot etalon is considered for degraded estimation of SBS spectral shifts and therefore approximate plasma-flow and ion-temperature information. The estimated cost of the new optical diagnostic channel is approximately €750–2,000, or up to approximately €2,500 including a second-hand Fabry-Pérot etalon, excluding the hBN sensor and acquisition hardware already considered in the previous work. The architecture deliberately does not attempt to reproduce the performance of dedicated FABS/SABS stations at major laser facilities. Instead, it asks a more modest question: How much scientifically useful diagnostic information can be recovered with a deliberately degraded but coherent and testable system? The proposed system aims to retain: time-resolved SBS/SRS reflectivity; coarse SBS/SRS spectral discrimination; correlation between optical and Villari/hBN signals; shot-to-shot anomaly detection; progressive extraction of diagnostic features; and, optionally, degraded estimates of plasma flow and ion temperature. The machine-learning component is explicitly formulated as an inter-shot analysis pipeline. The relevant laser-plasma processes occur on nanosecond to sub-microsecond timescales, making intra-shot corrective control unrealistic for the proposed architecture. Machine learning is therefore intended to learn from the accumulated history of previous shots and assist interpretation or prediction of subsequent shots. Because an independent platform may initially have only tens of shots rather than hundreds or thousands, the proposed analysis starts with simple statistical approaches such as regularized regression, classification and gradient-boosting methods rather than deep neural networks. This work is a methodological proposal and feasibility study. No experimental dataset has yet been acquired and no experimental validation is claimed. The informal name “Fusion 2CV” reflects the philosophy of the project: the objective is not to pretend that a €2,500 system is equivalent to a multi-million-euro diagnostic station, but to investigate how far a deliberately simple and inexpensive architecture can go.
Author: Lozac'h, Jean-yves
Language: French
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213160281