Paper | Title | Other Keywords | Page |
---|---|---|---|
MOPP031 | Optimisation of the ISIS Proton Synchrotron Experimental Damping System | kicker, synchrotron, feedback, betatron | 167 |
|
|||
The ISIS Neutron and Muon Source, located in the UK, consists of a H− linear accelerator, a rapid cycling proton synchrotron and two extraction lines delivering protons onto heavy metal targets. One of the limiting factors for achieving higher intensities in the accelerator is the head-tail instability present in the synchrotron, around 2ms after injection. In order to mitigate this instability, an experimental damping system is being developed for the ISIS synchrotron. Initial tests using a split electrode BPM as a pickup and a ferrite loaded kicker as a damper showed positive results. This paper describes the different developments made to the damping system and planned improvements to optimize its performance for use in normal operations. | |||
Poster MOPP031 [1.557 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-MOPP031 | ||
About • | paper received ※ 04 September 2019 paper accepted ※ 09 September 2019 issue date ※ 10 November 2019 | ||
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
TUPP018 | Synchrotron Radiation Monitor for SuperKEKB Damping Ring in Phase-III Operation | operation, injection, positron, MMI | 336 |
|
|||
The SuperKEKB damping ring (DR) commissioned in March 2019, before main ring (MR) Phase-III operation. The design luminosity of SuperKEKB is 40 times that of KEKB with high current and low emittance. We constructed the DR in order to deliver a low-emittance positron beam. A synchrotron radiation monitor (SRM) was installed for beam diagnostics at the DR. Streak camera and gated camera were used for measurement of the damping time and the beam size. This paper shows the design of DR SRM and the result of the measurement. | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-TUPP018 | ||
About • | paper received ※ 04 September 2019 paper accepted ※ 08 September 2019 issue date ※ 10 November 2019 | ||
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
TUPP040 | Digital Cameras for Photon Diagnostics at the Advanced Photon Source | controls, injection, detector, diagnostics | 425 |
|
|||
Funding: This research used resources of the Advanced Photon Source, operated for the U.S. Department of Energy Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Cameras can be a very useful accelerator diagnostic, particularly because an image of the beam distribution can be quickly interpreted by human operators, and increasingly can serve as an input to machine learning algorithms. We present an implementation of digital cameras for triggered photon diagnostics at the Advanced Photon Source using the areaDetector framework in the Experimental Physics and Industrial Controls System. Beam size measurements from the synchrotron light monitors in the Particle Accumulator Ring using the new architecture are presented. |
|||
Poster TUPP040 [0.708 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-TUPP040 | ||
About • | paper received ※ 04 September 2019 paper accepted ※ 08 September 2019 issue date ※ 10 November 2019 | ||
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
WEPP021 | Machine Learning Image Processing Technology Application in Bunch Longitudinal Phase Data Information Extraction | network, injection, synchrotron, SRF | 568 |
|
|||
To achieve the bunch-by-bunch longitudinal phase measurement, Shanghai Synchrotron Radiation Facility (SSRF) has developed a high resolution measurement system. We used this measurement system to study the injection transient process, and obtained the longitudinal phase of the refilled bunch and the longitudinal phase of the original stored bunch. A large number of parameters of the synchronous damping oscillation are included in this large amount of longitudinal phase data, which are important for the evaluation of machine state and bunch stability. The multi-turn phase data of a multi-bunch is a large two-dimensional array that can be converted into an image. The convolutional neural network (CNN) is a machine learning model with strong capabilities in image processing. We hope to use the convolutional neural network to process the longitudinal phase two-dimensional array data, and extract important parameters such as the oscillation amplitude and the synchrotron damping time. | |||
Poster WEPP021 [1.292 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-WEPP021 | ||
About • | paper received ※ 23 August 2019 paper accepted ※ 10 September 2019 issue date ※ 10 November 2019 | ||
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||