Applying fully convolutional networks for beam profile and emittance measurements
The transverse cross-sectional size and emittance are critical beam parameters that characterize the performance of the accelerator and assess the state of the beam. Inspired by the success of machine learning in image processing tasks, we have crafted a bespoke measurement system with a primary foc...
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Veröffentlicht in: | Journal of instrumentation 2023-10, Vol.18 (10), p.P10039 |
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Sprache: | eng |
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Zusammenfassung: | The transverse cross-sectional size and emittance are
critical beam parameters that characterize the performance of the
accelerator and assess the state of the beam. Inspired by the
success of machine learning in image processing tasks, we have
crafted a bespoke measurement system with a primary focus on
accurately determine the transverse cross-sectional size and
emittance of the beam. The system utilizes a beam spot detector to
convert the beam spot to a light spot image, which is then projected
onto the CCD camera through the telecentric lens for the
acquisition. The image data collected by the camera is subsequently
imported into the EPICS database developed based on ADAravis
software. We employ the Gaussian fitting technique on the collected
images to accurately calculate the cross-sectional size of the
beam. Furthermore, by incorporating the four-level iron scanning
method, the lateral emittance of the beam is calculated in a
comprehensive manner. To suppress the salt and pepper noise
introduced due to the presence of dark current and beam shooting
phenomena on the transmission line, we propose a novel fully
convolutional neural network (FCN) design with preactivated residual
units. The test conducted at HLS-II confirms that the measurement
uncertainty of this system is superior to 27.5 μm. Moreover,
when operating at an electron beam energy of 800 MeV, the measured
emittance of the accelerator is found to be 38.515 nm·rad, a
value closely aligning with the theoretical value of
36.2 nm·rad. These compelling results provide strong evidence
supporting the reliability of the emittance measurement algorithm,
making it suitable for deployment in the forthcoming terahertz
accelerator. |
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ISSN: | 1748-0221 1748-0221 |
DOI: | 10.1088/1748-0221/18/10/P10039 |