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Data Acquisition Components

Non-linear Multidimensional Optimization
for use in Wire Scanner Fitting

Student: Alyssa Henderson

School: University of Virginia

Mentored By: Alicia Hofler and Balša Terzić

To ensure experiment efficiency and quality from the Continuous Electron Beam Accelerator, beam energy, size, and position must be measured. Wire scanners are inserted into the beamline to produce measurements which can obtain beam properties. Extracting physical information from wire scanner measurements begins by fitting Gaussian curves to the data. This study focuses on optimizing and automating this curve-fitting procedure. We use a hybrid approach combining the efficiency of Newton Conjugate Gradient (NCG) method with the global convergence of three nature-inspired (NI) optimization approaches, which ensures the quality, robustness, and automation of curve-fitting. Given an initial data-derived guess, each finds a solution with the same chi-square-- a measurement of the data-to-fit agreement. The procedure begins with NCG and escalates to a NI method only if NCG fails, thereby ensuring a successful fit. This method allows for the most optimal signal fit and can be easily applied to similar problems.
Non-linear Multidimensional Optimization<br>for use in Wire Scanner Fitting