1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Machine learning-driven longitudinal phase space reconstruction for enhanced beam tuning at LANSCE

THPM024
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Petr Anisimov (Los Alamos National Laboratory)

Description

The Los Alamos Neutron Science Center (LANSCE) relies on accurate tuning of its Drift Tube Linacs (DTLs) to maintain beam quality and operational efficiency. This work introduces a novel machine-learning-based approach to reconstruct the longitudinal phase space (LPS) at the entrance of DTL Tank 1 using two-dimensional phase scans from Tanks 1 and 2. A Deep Neural Network trained on synthetic datasets generated by GPU-accelerated simulations integrates real-time diagnostic data to infer high-resolution LPS distributions. By solving this inverse problem efficiently, the method improves beam delivery precision while reducing operator intervention. Early results indicate that this approach can enhance LANSCE’s operational capabilities, providing a robust framework for accelerator tuning and diagnostics.

Funding Agency

Laboratory Directed Research and Development program of Los Alamos National Laboratory under project number 20250783MFR .

Footnotes

LA-UR-24-33003

Region represented America
Paper preparation format LaTeX

Author

Petr Anisimov (Los Alamos National Laboratory)

Presentation materials

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