Paper | Title | Page |
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MOPWI016 | Development of a Versatile Bunch-length Monitor for Electron Beams at ASTA | 1181 |
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Funding: Work at Fermilab supported by Fermi Research Alliance, LLC under Contract No. DE-AC02- 07CH11359 with the United States Department of Energy. The generation of bright electron beams at the ASTA/IOTA facility at Fermilab includes implementation of a versatile bunch-length monitor located after the 4-dipole chicane bunch compressor for electron beam energies of 20-50 MeV and integrated charges in excess of 10 nC. The station will include both a Hamamatsu C5680 synchroscan streak camera and a Martin-Puplett interferometer (MPI). An Al-coated Si screen will be used to generate both optical transition radiation (OTR) and coherent transition radiation (CTR) during the beam’s interaction with the screen. A chicane bypass beamline will allow the measurement of the initial bunch length at the same downstream beamline location using OTR and the streak camera. The UV component of the drive laser has previously been characterized with a Gaussian fit σ of 3.5 ps*, and the uncompressed electron beam is expected to be similar to this value at low charge per micropulse. In addition, OTR will be transported to the streak camera from the focal plane of the downstream spectrometer to provide an E-t distribution within the micropulse time scale. Commissioning of the system and initial results with beam will be presented as available. *A.H. Lumpkin et al., Proceedings of FEL14, MOP021, Basel, Switzerland, www. JACoW.org. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2015-MOPWI016 | |
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MOPWI028 | Initial Experimental Results of a Machine Learning-Based Temperature Control System for an RF Gun | 1217 |
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Colorado State University (CSU) and Fermi National Accelerator Laboratory (Fermilab) have been developing a control system to regulate the resonant frequency of an RF electron gun. As part of this effort, we present experimental results for a benchmark temperature controller that combines a machine learning-based model and a predictive control algorithm for improved settling time, overshoot, and disturbance rejection relative to conventional techniques. Such improvements have implications for machine up-time and management of reflected power. This work is part of an on-going effort to develop adaptive, machine learning-based tools specifically to address control challenges found in particle accelerator systems. | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2015-MOPWI028 | |
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TUPJE080 | First Beam and High-Gradient Cryomodule Commissioning Results of the Advanced Superconducting Test Accelerator at Fermilab | 1831 |
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Funding: Operated by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the United States Department of Energy. The advanced superconducting test accelerator at Fermilab has accelerated electrons to 20 MeV and, separately, the International Linear Collider (ILC) style 8-cavity cryomodule has achieved the ILC performance milestone of 31.5 MV/m per cavity. When fully completed, the accelerator will consist of a photoinjector, one ILC-type cryomodule, multiple accelerator R&D beamlines, and a downstream beamline to inject 300 MeV electrons into the Integrable Optics Test Accelerator (IOTA). We report on the results of first beam, the achievement of our cryomodule to ILC gradient specifications, and near-term future plans for the facility. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2015-TUPJE080 | |
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