IPAC2021 - Classification: MC6: Beam Instrumentation, Controls, Feedback and Operational Aspects / T33 Online Modeling and Software Tools

Paper	Title	Page
TUPAB329	Pattern Based Parameter Setup of the SNS Linac	2276
	C.C. Peters ORNL RAD, Oak Ridge, Tennessee, USA A.P. Shishlo ORNL, Oak Ridge, Tennessee, USA
	Funding: ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy. Theoretical and practical aspects of beam tuning procedures used for the SNS linac are discussed. The SNS linac includes two sections of beam acceleration. Acceleration in the first section up to 185.5 MeV is done with a room temperature copper linac which consists of both Drift Tube Linac (DTL) and Coupled Cavity Linac (CCL) Radio Frequency (RF) cavities. The second section consists of 81 Superconducting RF (SRF) cavities which accelerate the beam to the final beam energy of 1 GeV. The linac is currently capable of delivering an average beam power output of 1.44 MW with typical yearly operating hours of around 4500 hours. Due to the high power output and high availability of the linac, activation of accelerator equipment is a significant concern. The linac tuning process consists of three stages: model based setup of amplitudes and phases of the RF cavities, empirical beam loss reduction, and then documentation of the final amplitudes and phases of RF cavities after the empirical tuning. The final step is needed to ensure fast recovery from an SRF cavity failure. This paper discusses models, algorithms, diagnostic tools, software, and practices that are used for these stages.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB329
About •	paper received ※ 22 May 2021 paper accepted ※ 28 May 2021 issue date ※ 25 August 2021
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WEPAB183	Big Data Techniques for Accelerator Optimization	3039
	C.P. Welsch The University of Liverpool, Liverpool, United Kingdom C.P. Welsch Cockcroft Institute, Warrington, Cheshire, United Kingdom
	Funding: This project has received funding from STFC under grant reference ST/P006752/1. Accelerators and the experiments that they enable are some of the largest, most data-intensive, and most complex scientific systems in existence. The interrelations between machine subsystems are complicated and often nonlinear. The system dynamics involve large parameter spaces that evolve over multiple relevant time scales and accelerator systems. Any accelerator-based experiments and applications are almost always difficult to model. LIV. DAT, the Liverpool Centre for Doctoral Training in Data-intensive science, was established in 2017 as a hub for training students in Big Data science. The centre currently has 36 PhD students that are working across nuclear, particle and astrophysics, as well as in accelerator science. This paper presents results from R&D into betatron radiation models and beam parameter reconstruction for plasma acceleration experiments at FACET-II, simulations for MeV energy gain in dielectric structures driven by a CO₂ laser, and modelling of seeded self-modulation of long elliptical bunches in plasma. It also gives an overview of the training program offered to the LIV. DAT students.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB183
About •	paper received ※ 16 May 2021 paper accepted ※ 16 June 2021 issue date ※ 23 August 2021
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WEPAB302	COSY Machine-Model Optimization	3375
	I. Bekman, J.H. Hetzel FZJ, Jülich, Germany
	Funding: Helmholtz Association Successful operation of a particle accelerator requires accompanying model calculations. The model helps in understanding the machine and predicts the impact of a change in the settings (e.g. current of magnetic elements). For the COoler SYnchrotron (COSY) at Research Center Jülich the accelerator simulation software MAD-X is used to model the machine. The model parameters are steadily being improved based on various manual adjustments and analytical studies, however are hardly optimized all at once. This can be improved with machine learning methods. The model is used to predict measurable quantities, like Orbit Response Matrix (ORM) or betatron tunes. Several observables for different particle energies have been measured recently and the corresponding machine settings are available. We describe the effort to improve the agreement between measured and calculated ORMs and hence improve the agreement between model and (real) machine and report on the optimization using a multivariate algorithm (e.g. genetic algorithm). This facilitates the setup of COSY and will allow to perform high precision experiments e.g. a measurement of an electric dipole moment of deuterons at COSY.
	Poster WEPAB302 [1.905 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB302
About •	paper received ※ 14 May 2021 paper accepted ※ 28 July 2021 issue date ※ 20 August 2021
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WEPAB303	Machine Learning Applied to Automated Tunes Control at the 1.5 GeV Synchrotron Light Source DELTA	3379
	D. Schirmer DELTA, Dortmund, Germany
	Machine learning (ML) driven algorithms are finding more and more use cases in the domain of accelerator physics. Apart from correlation analysis in large data volumes, low and high level controls, like beam orbit correction, also non-linear feedback systems are possible application fields. This also includes monitoring the storage ring betatron tunes, as an important task for stable machine operation. For this purpose classical, shallow (non-deep), feed-forward neural networks (NNs) were investigated for automated adjusting the storage ring tunes. The NNs were trained with experimental machine data as well as with simulated data based on a lattice model of the DELTA storage ring. With both data sources comparable tune correction accuracies were achieved, both, in real machine operation and for the simulated storage ring model. In contrast to conventional PID methods, the trained NNs were able to approach the desired target tunes in fewer steps. The report summarizes the current status of this machine learning project and points out possible future improvements as well as other possible applications.
	Poster WEPAB303 [1.575 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB303
About •	paper received ※ 19 May 2021 paper accepted ※ 05 July 2021 issue date ※ 25 August 2021
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WEPAB304	Multi-Objective Multi-Generation Gaussian Process Optimizer	3383
	X. Huang, M. Song, Z. Zhang SLAC, Menlo Park, California, USA
	Funding: DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR. We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB304
About •	paper received ※ 17 May 2021 paper accepted ※ 12 July 2021 issue date ※ 28 August 2021
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WEPAB305	Teeport: Break the Wall Between the Optimization Algorithms and Problems	3387
	Z. Zhang, X. Huang, M. Song SLAC, Menlo Park, California, USA
	Funding: DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR. Optimization algorithms/techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and Gaussian process (GP) have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, sometimes even unrealistic, since the algorithms and problems could be implemented in different languages, might require specific resources, or have physical constraints. We introduce an optimization platform named Teeport that is developed to address the above issue. This real-time communication (RTC) based platform is particularly designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB305
About •	paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 27 August 2021
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WEPAB306	Applying Machine Learning to Optimization of Cooling Rate at Low Energy RHIC Electron Cooler	3391
	Y. Gao, K.A. Brown, P.S. Dyer, S. Seletskiy, H. Zhao BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The Low Energy RHIC electron Cooler (LEReC) is a novel, state-of-the-art, electron accelerator for cooling RHIC ion beams, which was recently built and commissioned. Optimization of cooling with LEReC requires fine-tuning of numerous LEReC parameters. In this work, initial optimization results of using Machine Learning (ML) methods - Bayesian Optimization (BO) and Q-learning are presented. Specially, we focus on exploring the influence of the electron trajectory on the cooling rate. In the first part, simulations are conducted by utilizing a LEReC simulator. The results show that both methods have the capability of deriving electron positions that can optimize the cooling rate. Moreover, BO takes fewer samples to converge than the Q-learning method. In the second part, Bayesian optimization is further trained on the historical cooling data. In the new samples generated by the BO, the percentage of larger cooling rates data is greatly enhanced compared with the original historical data.
	Poster WEPAB306 [1.083 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB306
About •	paper received ※ 12 May 2021 paper accepted ※ 01 July 2021 issue date ※ 24 August 2021
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WEPAB308	Measurement-Based Surrogate Model of the SLAC LCLS-II Injector	3395
	L. Gupta, Y.K. Kim University of Chicago, Chicago, Illinois, USA A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu SLAC, Menlo Park, California, USA
	Funding: This project was funded by the DOE SCGSR Program. There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308
About •	paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021
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WEPAB309	Study and Design of the Appropriate High-Performance Computing System for Beamline Data Analysis Application at Iranian Light Source Facility (ILSF)	3399
	A. Khaleghi, M. Akbari ILSF, Tehran, Iran H. Haedar, K. Mahmoudi, M. Takhttavani IKIU, Qazvin, Iran S. Mahmoudi Sharif University of Technology (SUT), Tehran, Iran
	Data analysis is a very important step in doing experiments at light sources, where multiple application and software packages are used for this purpose. In this paper we have reviewed some software packages that are used for data analysis and design at Iranian Light Source Facility then according to their processing needs, after taking in mind different HPC scenarios a suitable architecture for deployment of the ILSF HPC is presented. The proposed architecture is a cluster of 64 computing nodes connected through Ethernet and InfiniBand network running a Linux operating system with support of MPI parallel environment.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB309
About •	paper received ※ 19 May 2021 paper accepted ※ 23 July 2021 issue date ※ 01 September 2021
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WEPAB310	Study and Design of a High-Performance Computing Infrastructure for Iranian Light Source Facility Based on the Accelerator Physicists and Engineers’ Applications Requirements	3402
	K. Mahmoudi, H. Haedar, A. Khaleghi IKIU, Qazvin, Iran M. Akbari, A. Khaleghi ILSF, Tehran, Iran S. Mahmoudi IUST, Narmac, Tehran, Iran
	Synchrotron design and operation are one of the complex tasks which requires a lot of precise computation. As an example, we could mention the simulations done for calculating the impedance budget of the machine which requires a notable amount of computational power. In this paper we are going to review different HPC scenarios suitable for this matter then we will present our design of a suitable HPC based on the accelerator physicists and engineers’ needs. Going through different HPC scenarios such as shared memory architectures, distributed memory architectures, cluster, grid and cloud computing we conclude implementation of a dedicated computing cluster can be desired for ILSF. Cluster computing provides the opportunity for easy and saleable scientific computation for ILSF also another advantage is that its resources can be used for running cloud or grid computing platforms as well.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB310
About •	paper received ※ 19 May 2021 paper accepted ※ 19 July 2021 issue date ※ 12 August 2021
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WEPAB315	360 Degree Panoramic Photographs During the Long Shutdown 2 of the CERN Machines and Facilities	3410
	T.W. Birtwistle, A. Ansel, S. Bartolomé Jiménez, B. Feral, G. Lacerda, A.-L. Perrot, J.F. Piñera Ovejero CERN, Geneva, Switzerland
	Studies and preparation of activities are key to the success of short technical stops and longer shutdowns in CERN’s accelerator complex. The ’Panorama’ tool offers a virtual tour of our facilities, and thanks to integration with other CERN tools, further complementary information can be easily retrieved, including layout information, equipment detail, and a history of changes. The tool was used to support the preparation and the execution of works during the Long Shutdown 2. It helped to optimize machine (accelerator/decelerator) interventions and hence reduce potential radiation exposure, as well as to ease integration studies. Thanks to its user-friendliness, the tool is now also used for educational and outreach activities. The current instantiation of the ’Panorama’ tool and related processes is presented, alongside the benefits that the tool can bring to the accelerator complex community. A particular focus is on the Long Shutdown 2. Future planned developments and improvements are also described.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB315
About •	paper received ※ 11 May 2021 paper accepted ※ 14 June 2021 issue date ※ 21 August 2021
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WEPAB317	Online Model Developments for BESSY II and MLS	3413
	P. Schnizer, J. Bengtsson, T. Birke, J. Li, T. Mertens, M. Ries, A. Schälicke, L. Vera Ramirez HZB, Berlin, Germany
	Digital models have been developed over a long time for preparing accelerator commissioning next to benchmarking theory predictions to machine measurements. These digital models are nowadays being realized as digital shadows or digital twins. Accelerator commissioning requires periodic setup and review of the machine status. Furthermore, different measurements are only practical by comparison to the machine model (e.g. beam based alignment). In this paper we describe the architecture chosen for our models, describe the framework Bluesky for measurement orchestration and report on our experience exemplifying on dynamic aperture scans. Furthermore we describe our plans to extend the models applied to BESSY~II and MLS to the currently planned machines BESSY~III and MLS~II.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB317
About •	paper received ※ 19 May 2021 paper accepted ※ 28 July 2021 issue date ※ 21 August 2021
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WEPAB318	Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm	3417
	M. Maheshwari STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom D.J. Dunning, J.K. Jones, M.P. King, H.R. Kockelbergh, A.E. Pollard STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
	Machine learning algorithms were used for image and parameter recognition and generation with the aim to optimise the CLARA facility at Daresbury, using start-to-end simulation data. Convolutional and fully connected neural networks were trained using TensorFlow-Keras for different instances, with examples including predicting Longitudinal Phase Space (LPS) images with machine parameters as input and FEL parameter prediction (e.g. pulse energy) from LPS images. The K-means clustering algorithm was used to cluster the LPS images to highlight patterns within the data. Machine learning techniques can enhance the way large amounts of data are processed and analysed and so have great potential for application in accelerator science R&D.
	Poster WEPAB318 [1.062 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB318
About •	paper received ※ 17 May 2021 paper accepted ※ 05 July 2021 issue date ※ 21 August 2021
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WEPAB319	Open XAL Status Report 2021	3421
	N. Milas, J.F. Esteban Müller, E. Laface, Y. Levinsen ESS, Lund, Sweden T.V. Gorlov, A.P. Shishlo, A.P. Zhukov ORNL, Oak Ridge, Tennessee, USA
	The Open XAL accelerator physics software platform is being developed through international collaboration among several facilities since 2010. The goal of the collaboration is to establish Open XAL as a multi-purpose software platform supporting a broad range of tool and application development in accelerator physics and high-level control (Open XAL also ships with a suite of general-purpose accelerator applications). This paper discusses progress in beam dynamics simulation, new RF models, and updated application framework along with new generic accelerator physics applications. We present the current status of the project, a roadmap for continued development, and an overview of the project status at each participating facility.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB319
About •	paper received ※ 19 May 2021 paper accepted ※ 21 July 2021 issue date ※ 11 August 2021
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WEPAB320	RecCeiver-ETCD: A Bridge Between ETCD and ChannelFinder	3424
	G. Jhang, T. Ashwarya, A. Carriveau FRIB, East Lansing, Michigan, USA
	Funding: Work supported by the U.S. Department of Energy Office of Science under Cooperative Agreement DE-SC0000661 Managing EPICS Process Variables’~(PVs) metadata, such as the host and the contact, is one of the important tasks for the operation of large-scale accelerator facilities with minimal downtime. Record Sychronizer~(RecSync) provides a way to manage such crucial information in an EPICS Input-Output Controller~(IOC). RecCeiver-ETCD is the server component of the RecSync-ETCD, or an extension of RecCeiver for ETCD. In the previous work, the client component of RecSync, or RecCaster, has been extended to RecCaster-ETCD to store the metadata into an ETCD key-value store. An important remaining step to the production use is to introduce a connection between ETCD and ChannelFinder, which is achieved by RecCeiver in the RecSync system. RecCeiver-ETCD plays the role of the original RecCeiver in the RecSync-ETCD system. RecCeiver-ETCD is designed to perform the specific operation, bridging the communication between ETCD and ChannelFinder. In addition, its simple implementation does not hold it down to ChannelFinder and makes it easy to extend RecCeiver-ETCD out to the other applications.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB320
About •	paper received ※ 11 May 2021 paper accepted ※ 19 July 2021 issue date ※ 17 August 2021
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THPAB138	FEbreak: A Comprehensive Diagnostic and Automated Conditioning Interface for Analysis of Breakdown and Dark Current Effects	4027
	M.E. Schneider, S.V. Baryshev Michigan State University, East Lansing, Michigan, USA R.L. Fleming, D. Gorelov, J.W. Lewellen, E.I. Simakov LANL, Los Alamos, New Mexico, USA E. Jevarjian MSU, East Lansing, Michigan, USA
	Funding: DE-AC02-06CH11357, No. DE-SC0018362, DE-NA-0003525, DE-AC52-06NA25396, LA-UR-21-20613 As the next generation of accelerator technology pushes towards being able to achieve higher and higher gradients there is a need to develop high-frequency structures that can support these fields . The conditioning process of the structures and waveguides to high gradient is a labor-intensive process, its length increases as the maximum gradient is increased. This results in the need to automate the conditioning process. This automation must allow for high accuracy calculations of the breakdown probabilities associated with the conditioning process which can be used to instruct the conditioning procedure without the need for human intervention. To automate the conditioning process at LANL’s high gradient C-band accelerator test stand we developed FEbreak that is a breakout probability and conditioning automation software that is a part of the FEmaster series , , *. FEbreak directly interfaces with the rest of FEmaster to automate the data collection and data processing to not only analyze the breakdown probability but also the dark current effects associated with these high gradient structures. E. I. Simakov Nuc. Inst. and Meth, in Phy. Research Section A: Acc. Spec, 907 221 (2019) E. Jevarjian arXiv:2009.13046 * T. Y. Posos arXiv:2012.03578 **** M. Schneider arXiv:2012.10804
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB138
About •	paper received ※ 18 May 2021 paper accepted ※ 02 July 2021 issue date ※ 16 August 2021
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THPAB205	On-Line Retuning of ISAC Linac Beam with Quadrupole Scan Tomography	4187
	O. Shelbaya, R.A. Baartman, P.M. Jung, O.K. Kester, S. Kiy, T. Planche, Y.-N. Rao, S.D. Rädel TRIUMF, Vancouver, Canada
	The method of tomographic reconstruction has been in use at TRIUMF and elsewhere for several years, allowing for the beam diagnostic extraction of elements of the beam matrix on-line. One of the more recent applications of the technique at ISAC consists of using the measured density distribution as the input parameters for a real-time tune re-computation. This technique is advantageous since it does not require installation of dedicated emittance meters, but can instead be carried out with existing position monitors. Instead of requiring an operator to manually re-tune quadrupoles in a matching section, which can be time consuming, the technique allows for a fast and reproducible means to precisely control the beam and can be proceduralized for use by operators tuning the machine.
	Poster THPAB205 [0.468 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB205
About •	paper received ※ 18 May 2021 paper accepted ※ 08 July 2021 issue date ※ 10 August 2021
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THPAB260	Detection and Classification of Collective Beam Behaviour in the LHC	4318
	L. Coyle, F. Blanc, T. Pieloni, M. Schenk EPFL, Lausanne, Switzerland X. Buffat, M. Solfaroli Camillocci, J. Wenninger CERN, Meyrin, Switzerland E. Krymova, G. Obozinski SDSC, Lausanne, Switzerland
	Collective instabilities can lead to a severe deterioration of beam quality, in terms of reduced beam intensity and increased beam emittance, and consequently a reduction of the collider’s luminosity. It is therefore crucial for the operation of the CERN’s Large Hadron Collider to understand the conditions in which they appear in order to find appropriate mitigation measures. Using bunch-by-bunch and turn-by-turn beam amplitude data, courtesy of the transverse damper’s observation box (ObsBox), a novel machine learning based approach is developed to both detect and classify these instabilities. By training an autoencoder neural network on the ObsBox amplitude data and using the model’s reconstruction error, instabilities and other phenomena are separated from nominal beam behaviour. Additionally, the latent space encoding of this autoencoder offers a unique image like representation of the beam amplitude signal. Leveraging this latent space representation allows us to cluster the various types of anomalous signals.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB260
About •	paper received ※ 19 May 2021 paper accepted ※ 19 July 2021 issue date ※ 27 August 2021
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Paper

Title

Page

Pattern Based Parameter Setup of the SNS Linac

2276

C.C. Peters
ORNL RAD, Oak Ridge, Tennessee, USA
A.P. Shishlo
ORNL, Oak Ridge, Tennessee, USA

Funding: ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy.
Theoretical and practical aspects of beam tuning procedures used for the SNS linac are discussed. The SNS linac includes two sections of beam acceleration. Acceleration in the first section up to 185.5 MeV is done with a room temperature copper linac which consists of both Drift Tube Linac (DTL) and Coupled Cavity Linac (CCL) Radio Frequency (RF) cavities. The second section consists of 81 Superconducting RF (SRF) cavities which accelerate the beam to the final beam energy of 1 GeV. The linac is currently capable of delivering an average beam power output of 1.44 MW with typical yearly operating hours of around 4500 hours. Due to the high power output and high availability of the linac, activation of accelerator equipment is a significant concern. The linac tuning process consists of three stages: model based setup of amplitudes and phases of the RF cavities, empirical beam loss reduction, and then documentation of the final amplitudes and phases of RF cavities after the empirical tuning. The final step is needed to ensure fast recovery from an SRF cavity failure. This paper discusses models, algorithms, diagnostic tools, software, and practices that are used for these stages.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB329

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paper received ※ 22 May 2021 paper accepted ※ 28 May 2021 issue date ※ 25 August 2021

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WEPAB183

Big Data Techniques for Accelerator Optimization

3039

C.P. Welsch
The University of Liverpool, Liverpool, United Kingdom
C.P. Welsch
Cockcroft Institute, Warrington, Cheshire, United Kingdom

Funding: This project has received funding from STFC under grant reference ST/P006752/1.
Accelerators and the experiments that they enable are some of the largest, most data-intensive, and most complex scientific systems in existence. The interrelations between machine subsystems are complicated and often nonlinear. The system dynamics involve large parameter spaces that evolve over multiple relevant time scales and accelerator systems. Any accelerator-based experiments and applications are almost always difficult to model. LIV. DAT, the Liverpool Centre for Doctoral Training in Data-intensive science, was established in 2017 as a hub for training students in Big Data science. The centre currently has 36 PhD students that are working across nuclear, particle and astrophysics, as well as in accelerator science. This paper presents results from R&D into betatron radiation models and beam parameter reconstruction for plasma acceleration experiments at FACET-II, simulations for MeV energy gain in dielectric structures driven by a CO₂ laser, and modelling of seeded self-modulation of long elliptical bunches in plasma. It also gives an overview of the training program offered to the LIV. DAT students.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB183

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paper received ※ 16 May 2021 paper accepted ※ 16 June 2021 issue date ※ 23 August 2021

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WEPAB302

COSY Machine-Model Optimization

3375

I. Bekman, J.H. Hetzel
FZJ, Jülich, Germany

Funding: Helmholtz Association
Successful operation of a particle accelerator requires accompanying model calculations. The model helps in understanding the machine and predicts the impact of a change in the settings (e.g. current of magnetic elements). For the COoler SYnchrotron (COSY) at Research Center Jülich the accelerator simulation software MAD-X is used to model the machine. The model parameters are steadily being improved based on various manual adjustments and analytical studies, however are hardly optimized all at once. This can be improved with machine learning methods. The model is used to predict measurable quantities, like Orbit Response Matrix (ORM) or betatron tunes. Several observables for different particle energies have been measured recently and the corresponding machine settings are available. We describe the effort to improve the agreement between measured and calculated ORMs and hence improve the agreement between model and (real) machine and report on the optimization using a multivariate algorithm (e.g. genetic algorithm). This facilitates the setup of COSY and will allow to perform high precision experiments e.g. a measurement of an electric dipole moment of deuterons at COSY.

Poster WEPAB302 [1.905 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB302

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paper received ※ 14 May 2021 paper accepted ※ 28 July 2021 issue date ※ 20 August 2021

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WEPAB303

Machine Learning Applied to Automated Tunes Control at the 1.5 GeV Synchrotron Light Source DELTA

3379

D. Schirmer
DELTA, Dortmund, Germany

Machine learning (ML) driven algorithms are finding more and more use cases in the domain of accelerator physics. Apart from correlation analysis in large data volumes, low and high level controls, like beam orbit correction, also non-linear feedback systems are possible application fields. This also includes monitoring the storage ring betatron tunes, as an important task for stable machine operation. For this purpose classical, shallow (non-deep), feed-forward neural networks (NNs) were investigated for automated adjusting the storage ring tunes. The NNs were trained with experimental machine data as well as with simulated data based on a lattice model of the DELTA storage ring. With both data sources comparable tune correction accuracies were achieved, both, in real machine operation and for the simulated storage ring model. In contrast to conventional PID methods, the trained NNs were able to approach the desired target tunes in fewer steps. The report summarizes the current status of this machine learning project and points out possible future improvements as well as other possible applications.

Poster WEPAB303 [1.575 MB]

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB303

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paper received ※ 19 May 2021 paper accepted ※ 05 July 2021 issue date ※ 25 August 2021

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WEPAB304

Multi-Objective Multi-Generation Gaussian Process Optimizer

3383

X. Huang, M. Song, Z. Zhang
SLAC, Menlo Park, California, USA

Funding: DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR.
We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB304

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paper received ※ 17 May 2021 paper accepted ※ 12 July 2021 issue date ※ 28 August 2021

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WEPAB305

Teeport: Break the Wall Between the Optimization Algorithms and Problems

3387

Z. Zhang, X. Huang, M. Song
SLAC, Menlo Park, California, USA

Funding: DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR.
Optimization algorithms/techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and Gaussian process (GP) have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, sometimes even unrealistic, since the algorithms and problems could be implemented in different languages, might require specific resources, or have physical constraints. We introduce an optimization platform named Teeport that is developed to address the above issue. This real-time communication (RTC) based platform is particularly designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB305

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paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 27 August 2021

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WEPAB306

Applying Machine Learning to Optimization of Cooling Rate at Low Energy RHIC Electron Cooler

3391

Y. Gao, K.A. Brown, P.S. Dyer, S. Seletskiy, H. Zhao
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
The Low Energy RHIC electron Cooler (LEReC) is a novel, state-of-the-art, electron accelerator for cooling RHIC ion beams, which was recently built and commissioned. Optimization of cooling with LEReC requires fine-tuning of numerous LEReC parameters. In this work, initial optimization results of using Machine Learning (ML) methods - Bayesian Optimization (BO) and Q-learning are presented. Specially, we focus on exploring the influence of the electron trajectory on the cooling rate. In the first part, simulations are conducted by utilizing a LEReC simulator. The results show that both methods have the capability of deriving electron positions that can optimize the cooling rate. Moreover, BO takes fewer samples to converge than the Q-learning method. In the second part, Bayesian optimization is further trained on the historical cooling data. In the new samples generated by the BO, the percentage of larger cooling rates data is greatly enhanced compared with the original historical data.

Poster WEPAB306 [1.083 MB]

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB306

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paper received ※ 12 May 2021 paper accepted ※ 01 July 2021 issue date ※ 24 August 2021

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WEPAB308

Measurement-Based Surrogate Model of the SLAC LCLS-II Injector

3395

L. Gupta, Y.K. Kim
University of Chicago, Chicago, Illinois, USA
A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu
SLAC, Menlo Park, California, USA

Funding: This project was funded by the DOE SCGSR Program.
There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308

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paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021

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WEPAB309

Study and Design of the Appropriate High-Performance Computing System for Beamline Data Analysis Application at Iranian Light Source Facility (ILSF)

3399

A. Khaleghi, M. Akbari
ILSF, Tehran, Iran
H. Haedar, K. Mahmoudi, M. Takhttavani
IKIU, Qazvin, Iran
S. Mahmoudi
Sharif University of Technology (SUT), Tehran, Iran

Data analysis is a very important step in doing experiments at light sources, where multiple application and software packages are used for this purpose. In this paper we have reviewed some software packages that are used for data analysis and design at Iranian Light Source Facility then according to their processing needs, after taking in mind different HPC scenarios a suitable architecture for deployment of the ILSF HPC is presented. The proposed architecture is a cluster of 64 computing nodes connected through Ethernet and InfiniBand network running a Linux operating system with support of MPI parallel environment.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB309

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paper received ※ 19 May 2021 paper accepted ※ 23 July 2021 issue date ※ 01 September 2021

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WEPAB310

Study and Design of a High-Performance Computing Infrastructure for Iranian Light Source Facility Based on the Accelerator Physicists and Engineers’ Applications Requirements

3402

K. Mahmoudi, H. Haedar, A. Khaleghi
IKIU, Qazvin, Iran
M. Akbari, A. Khaleghi
ILSF, Tehran, Iran
S. Mahmoudi
IUST, Narmac, Tehran, Iran

Synchrotron design and operation are one of the complex tasks which requires a lot of precise computation. As an example, we could mention the simulations done for calculating the impedance budget of the machine which requires a notable amount of computational power. In this paper we are going to review different HPC scenarios suitable for this matter then we will present our design of a suitable HPC based on the accelerator physicists and engineers’ needs. Going through different HPC scenarios such as shared memory architectures, distributed memory architectures, cluster, grid and cloud computing we conclude implementation of a dedicated computing cluster can be desired for ILSF. Cluster computing provides the opportunity for easy and saleable scientific computation for ILSF also another advantage is that its resources can be used for running cloud or grid computing platforms as well.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB310

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paper received ※ 19 May 2021 paper accepted ※ 19 July 2021 issue date ※ 12 August 2021

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WEPAB315

360 Degree Panoramic Photographs During the Long Shutdown 2 of the CERN Machines and Facilities

3410

T.W. Birtwistle, A. Ansel, S. Bartolomé Jiménez, B. Feral, G. Lacerda, A.-L. Perrot, J.F. Piñera Ovejero
CERN, Geneva, Switzerland

Studies and preparation of activities are key to the success of short technical stops and longer shutdowns in CERN’s accelerator complex. The ’Panorama’ tool offers a virtual tour of our facilities, and thanks to integration with other CERN tools, further complementary information can be easily retrieved, including layout information, equipment detail, and a history of changes. The tool was used to support the preparation and the execution of works during the Long Shutdown 2. It helped to optimize machine (accelerator/decelerator) interventions and hence reduce potential radiation exposure, as well as to ease integration studies. Thanks to its user-friendliness, the tool is now also used for educational and outreach activities. The current instantiation of the ’Panorama’ tool and related processes is presented, alongside the benefits that the tool can bring to the accelerator complex community. A particular focus is on the Long Shutdown 2. Future planned developments and improvements are also described.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB315

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paper received ※ 11 May 2021 paper accepted ※ 14 June 2021 issue date ※ 21 August 2021

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WEPAB317

Online Model Developments for BESSY II and MLS

3413

P. Schnizer, J. Bengtsson, T. Birke, J. Li, T. Mertens, M. Ries, A. Schälicke, L. Vera Ramirez
HZB, Berlin, Germany

Digital models have been developed over a long time for preparing accelerator commissioning next to benchmarking theory predictions to machine measurements. These digital models are nowadays being realized as digital shadows or digital twins. Accelerator commissioning requires periodic setup and review of the machine status. Furthermore, different measurements are only practical by comparison to the machine model (e.g. beam based alignment). In this paper we describe the architecture chosen for our models, describe the framework Bluesky for measurement orchestration and report on our experience exemplifying on dynamic aperture scans. Furthermore we describe our plans to extend the models applied to BESSY~II and MLS to the currently planned machines BESSY~III and MLS~II.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB317

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paper received ※ 19 May 2021 paper accepted ※ 28 July 2021 issue date ※ 21 August 2021

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WEPAB318

Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm

3417

M. Maheshwari
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
D.J. Dunning, J.K. Jones, M.P. King, H.R. Kockelbergh, A.E. Pollard
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom

Machine learning algorithms were used for image and parameter recognition and generation with the aim to optimise the CLARA facility at Daresbury, using start-to-end simulation data. Convolutional and fully connected neural networks were trained using TensorFlow-Keras for different instances, with examples including predicting Longitudinal Phase Space (LPS) images with machine parameters as input and FEL parameter prediction (e.g. pulse energy) from LPS images. The K-means clustering algorithm was used to cluster the LPS images to highlight patterns within the data. Machine learning techniques can enhance the way large amounts of data are processed and analysed and so have great potential for application in accelerator science R&D.

Poster WEPAB318 [1.062 MB]

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB318

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paper received ※ 17 May 2021 paper accepted ※ 05 July 2021 issue date ※ 21 August 2021

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WEPAB319

Open XAL Status Report 2021

3421

N. Milas, J.F. Esteban Müller, E. Laface, Y. Levinsen
ESS, Lund, Sweden
T.V. Gorlov, A.P. Shishlo, A.P. Zhukov
ORNL, Oak Ridge, Tennessee, USA

The Open XAL accelerator physics software platform is being developed through international collaboration among several facilities since 2010. The goal of the collaboration is to establish Open XAL as a multi-purpose software platform supporting a broad range of tool and application development in accelerator physics and high-level control (Open XAL also ships with a suite of general-purpose accelerator applications). This paper discusses progress in beam dynamics simulation, new RF models, and updated application framework along with new generic accelerator physics applications. We present the current status of the project, a roadmap for continued development, and an overview of the project status at each participating facility.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB319

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paper received ※ 19 May 2021 paper accepted ※ 21 July 2021 issue date ※ 11 August 2021

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WEPAB320

RecCeiver-ETCD: A Bridge Between ETCD and ChannelFinder

3424

G. Jhang, T. Ashwarya, A. Carriveau
FRIB, East Lansing, Michigan, USA

Funding: Work supported by the U.S. Department of Energy Office of Science under Cooperative Agreement DE-SC0000661
Managing EPICS Process Variables’~(PVs) metadata, such as the host and the contact, is one of the important tasks for the operation of large-scale accelerator facilities with minimal downtime. Record Sychronizer~(RecSync) provides a way to manage such crucial information in an EPICS Input-Output Controller~(IOC). RecCeiver-ETCD is the server component of the RecSync-ETCD, or an extension of RecCeiver for ETCD. In the previous work, the client component of RecSync, or RecCaster, has been extended to RecCaster-ETCD to store the metadata into an ETCD key-value store. An important remaining step to the production use is to introduce a connection between ETCD and ChannelFinder, which is achieved by RecCeiver in the RecSync system. RecCeiver-ETCD plays the role of the original RecCeiver in the RecSync-ETCD system. RecCeiver-ETCD is designed to perform the specific operation, bridging the communication between ETCD and ChannelFinder. In addition, its simple implementation does not hold it down to ChannelFinder and makes it easy to extend RecCeiver-ETCD out to the other applications.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB320

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paper received ※ 11 May 2021 paper accepted ※ 19 July 2021 issue date ※ 17 August 2021

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THPAB138

FEbreak: A Comprehensive Diagnostic and Automated Conditioning Interface for Analysis of Breakdown and Dark Current Effects

4027

M.E. Schneider, S.V. Baryshev
Michigan State University, East Lansing, Michigan, USA
R.L. Fleming, D. Gorelov, J.W. Lewellen, E.I. Simakov
LANL, Los Alamos, New Mexico, USA
E. Jevarjian
MSU, East Lansing, Michigan, USA

Funding: DE-AC02-06CH11357, No. DE-SC0018362, DE-NA-0003525, DE-AC52-06NA25396, LA-UR-21-20613
As the next generation of accelerator technology pushes towards being able to achieve higher and higher gradients there is a need to develop high-frequency structures that can support these fields *. The conditioning process of the structures and waveguides to high gradient is a labor-intensive process, its length increases as the maximum gradient is increased. This results in the need to automate the conditioning process. This automation must allow for high accuracy calculations of the breakdown probabilities associated with the conditioning process which can be used to instruct the conditioning procedure without the need for human intervention. To automate the conditioning process at LANL’s high gradient C-band accelerator test stand we developed FEbreak that is a breakout probability and conditioning automation software that is a part of the FEmaster series **, ***, ****. FEbreak directly interfaces with the rest of FEmaster to automate the data collection and data processing to not only analyze the breakdown probability but also the dark current effects associated with these high gradient structures.
* E. I. Simakov Nuc. Inst. and Meth, in Phy. Research Section A: Acc. Spec, 907 221 (2019)
** E. Jevarjian arXiv:2009.13046
*** T. Y. Posos arXiv:2012.03578
**** M. Schneider arXiv:2012.10804

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB138

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paper received ※ 18 May 2021 paper accepted ※ 02 July 2021 issue date ※ 16 August 2021

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THPAB205

On-Line Retuning of ISAC Linac Beam with Quadrupole Scan Tomography

4187

O. Shelbaya, R.A. Baartman, P.M. Jung, O.K. Kester, S. Kiy, T. Planche, Y.-N. Rao, S.D. Rädel
TRIUMF, Vancouver, Canada

The method of tomographic reconstruction has been in use at TRIUMF and elsewhere for several years, allowing for the beam diagnostic extraction of elements of the beam matrix on-line. One of the more recent applications of the technique at ISAC consists of using the measured density distribution as the input parameters for a real-time tune re-computation. This technique is advantageous since it does not require installation of dedicated emittance meters, but can instead be carried out with existing position monitors. Instead of requiring an operator to manually re-tune quadrupoles in a matching section, which can be time consuming, the technique allows for a fast and reproducible means to precisely control the beam and can be proceduralized for use by operators tuning the machine.

Poster THPAB205 [0.468 MB]

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB205

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paper received ※ 18 May 2021 paper accepted ※ 08 July 2021 issue date ※ 10 August 2021

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THPAB260

Detection and Classification of Collective Beam Behaviour in the LHC

4318

L. Coyle, F. Blanc, T. Pieloni, M. Schenk
EPFL, Lausanne, Switzerland
X. Buffat, M. Solfaroli Camillocci, J. Wenninger
CERN, Meyrin, Switzerland
E. Krymova, G. Obozinski
SDSC, Lausanne, Switzerland

Collective instabilities can lead to a severe deterioration of beam quality, in terms of reduced beam intensity and increased beam emittance, and consequently a reduction of the collider’s luminosity. It is therefore crucial for the operation of the CERN’s Large Hadron Collider to understand the conditions in which they appear in order to find appropriate mitigation measures. Using bunch-by-bunch and turn-by-turn beam amplitude data, courtesy of the transverse damper’s observation box (ObsBox), a novel machine learning based approach is developed to both detect and classify these instabilities. By training an autoencoder neural network on the ObsBox amplitude data and using the model’s reconstruction error, instabilities and other phenomena are separated from nominal beam behaviour. Additionally, the latent space encoding of this autoencoder offers a unique image like representation of the beam amplitude signal. Leveraging this latent space representation allows us to cluster the various types of anomalous signals.

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reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB260

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paper received ※ 19 May 2021 paper accepted ※ 19 July 2021 issue date ※ 27 August 2021

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