Machine Learning for Radiographic Source Optimization at Linear Induction Accelerators*

Jason E. Koglin; Michael McKerns; Alex Scheinker; Dan Wakeford

doi:10.1364/3D.2023.JTu4A.38

Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)
Technical Digest Series (Optica Publishing Group, 2023),
paper JTu4A.38
•https://doi.org/10.1364/3D.2023.JTu4A.38

Machine Learning for Radiographic Source Optimization at Linear Induction Accelerators^*

Jason E. Koglin, Michael McKerns, Alex Scheinker, and Dan Wakeford

3D Image Acquisition and Display: Technology, Perception and Applications 2023

Boston, Massachusetts United States
14–17 August 2023
ISBN: 978-1-957171-28-9

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Joint Poster Session I (JTu4A)

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J. E. Koglin, M. McKerns, A. Scheinker, and D. Wakeford, "Machine Learning for Radiographic Source Optimization at Linear Induction Accelerators*," in Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), Technical Digest Series (Optica Publishing Group, 2023), paper JTu4A.38.

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Abstract

Adaptive machine learning (AML) techniques are being designed to use noninvasive diagnostic measurements to address the challenge of predicting the radiographic spot size, which depends on the accelerator performance and the conversion target.

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