IBIC2022 - List of Keywords (network)

Paper	Title	Other Keywords	Page
MOP10	Removing Noise in BPM Measurements with Variational Autoencoders	operation, optics, coupling, controls	43
	J.P. Edelen, J.A. Einstein-Curtis, C.C. Hall, M.J. Henderson RadiaSoft LLC, Boulder, Colorado, USA A.L. Romanov Fermilab, Batavia, Illinois, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699. Noise in beam measurements is an ever-present challenge in accelerator operations. In addition to the challenges presented by hardware and signal processing, new operational regimes, such as ultra-short bunches, create additional difficulties in routine beam measurements. Techniques in machine learning have been successfully applied in other domains to overcome challenges inherent in noisy data. Variational autoencoders (VAEs) are shown to be capable of removing significant leevels of noise. A VAE can be used as a pre-processing tool for noise removal before the de-noised data is analyzed via other methods, or the VAE can be directly used to make beam dynamics measurements. Here we present the use of VAEs as a tool for addressing noise in BPM measurements.
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP10
About •	Received ※ 29 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 24 November 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOP38	Beam Profile Monitoring and Distributed Analysis Using the RabbitMQ Message Broker	interface, software, controls, Ethernet	140
	D. Proft, K. Desch, D. Elsner, F. Frommberger, S. Kronenberg, A. Spreitzer, M.T. Switka ELSA, Bonn, Germany
	The ELSA facility utilizes several digital cameras for beam profile measurements on luminous screens and synchrotron radiation monitors. Currently a multitude of devices with analog signal output are being replaced in favor of digital outputs, preferably with data transfer via Ethernet. The increased network traffic for streaming, analyzing, and distribution of processed data to control system and machine operators is managed through a supplementary camera network in which distributed computing is performed by the RabbitMQ message broker. This allows performant and platform-independent image acquisition from multiple cameras, real time profile analysis, and supports programming interfaces for C++ and Python. The setup and performance of the implementation are presented.
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP38
About •	Received ※ 07 September 2022 — Revised ※ 09 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 28 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOP43	Web-Based Application for Cable Simulation Models	simulation, instrumentation, electron, impedance	156
	M.C. Paniccia, S.L. Clark, D.M. Gassner, R.L. Hulsart, P. Thieberger BNL, Upton, New York, USA
	Attenuation in a lossy coaxial cable increases over distance and varies over frequency. Having a model of these variations can help predict the expected loss and distortion of a signal. This paper discusses a free web-based application developed to provide accurate SPICE models for various coaxial cable types. The user can specify a length and select between different cable types, or upload their own cable attenuation curve, and receive a SPICE model for that cable. These simulation models have been used to assist the design and development of new instrumentation systems for the future Electron Ion Collider (EIC).
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP43
About •	Received ※ 06 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 13 September 2022 — Issue date ※ 22 November 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP38	Deep Neural Network for Beam Profile Classification in Synchrotron	diagnostics, operation, emittance, synchrotron	323
	M. Piekarski NSRC SOLARIS, Kraków, Poland
	Funding: The presented work has been achieved in collaboration with AGH University of Science and Technology in Kraków as a part of a PhD thesis. The main goal of NSRC SOLARIS is to provide scientific community with high quality synchrotron light. To achieve this, it is necessary to constantly monitor many subsystems responsible for beam stability and to analyze data about the beam itself from various diagnostic beamlines. In this work a deep neural network for transverse beam profile classification is proposed. Main task of the system is to automatically assess and classify transverse beam profiles based solely on the evaluation of the beam image from the Pinhole diagnostic beamline at SOLARIS. At the present stage, a binary assignment of each profile is performed: stable beam operation or unstable beam operation / no beam. Base model architecture consists of a pre-trained convolutional neural network followed by a densely-connected classifier and the system reaches accuracy at the level of 90%. The model and the results obtained so far are discussed, along with plans for future development.
	Poster TUP38 [0.376 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP38
About •	Received ※ 30 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 15 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP39	Neural Network Inverse Models for Implicit Optics Tuning in the AGS to RHIC Transfer Line	quadrupole, diagnostics, operation, controls	327
	J.P. Edelen, N.M. Cook, J.A. Einstein-Curtis RadiaSoft LLC, Boulder, Colorado, USA K.A. Brown, V. Schoefer BNL, Upton, New York, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682 One of the fundamental challenges of using machine-learning-based inverse models for optics tuning in accelerators, particularly transfer lines, is the degenerate nature of the magnet settings and beam envelope functions. Moreover, it is challenging, if not impossible, to train a neural network to compute correct quadrupole settings from a given set of measurements due to the limited number of diagnostics available in operational beamlines. However, models that relate BPM readings to corrector settings are more forgiving, and have seen significant success as a benchmark for machine learning inverse models. We recently demonstrated that when comparing predicted corrector settings to actual corrector settings from a BPM inverse model, the model error can be related to errors in quadrupole settings. In this paper, we expand on that effort by incorporating inverse model errors as an optimization tool to correct for optics errors in a beamline. We present a toy model using a FODO lattice and then demonstrate the use of this technique for optics corrections in the AGS to RHIC transfer line at BNL.
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP39
About •	Received ※ 05 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 12 November 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP42	Fast Orbit Feedback Upgrade at SOLEIL	interface, FPGA, controls, electron	339
	R. Broucquart, N. Hubert SOLEIL, Gif-sur-Yvette, France
	In the framework of the SOLEIL II project, the diagnostics group must anticipate ahead of the dark period the upgrade of important system like the BPM electronics, the timing system end the Fast Orbit Feedback (FOFB). The FOFB is a complex system that is currently embedded in the BPM electronics modules (eBPM). A new flexible stand-alone platform is under conception to follow the future upgrades of surrounding equipment, and to allow the integration of future correction schemes. In this paper we will present the current status of technical decisions, tests and developments.
	Poster TUP42 [3.305 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP42
About •	Received ※ 07 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 25 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEP40	A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation	Ethernet, software, instrumentation, data-acquisition	500
	R.R. Santucci, J.S. Diamond, N. Eddy, A. Semenov, D.C. Voy Fermilab, Batavia, Illinois, USA
	The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1GbE and aggregated at a switch which up linked to a 10GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10Gbit/s using off the shelf Ethernet technology.
	Poster WEP40 [0.738 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP40
About •	Received ※ 08 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 04 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEP42	Application of Machine Learning towards Particle Counting and Identification	Windows, extraction, detector, experiment	508
	S.E. Engel University of Essex, Physics Centre, Colchester, United Kingdom P. Boutachkov, R. Singh GSI, Darmstadt, Germany
	An exploration into the application of three machine learning (ML) approaches to identify and separate events in the detectors used for particle counting at the GSI Helmholtz Centre for Heavy Ion Research. A convolutional neural network (CNN), a shape-based template matching algorithm (STMF) and Peak Property-based Counting Algorithm (PPCA) were developed to accurately count the number of particles without domain-specific knowledge required to run the currently used algorithm. The three domain-agnostic ML algorithms are based on data from scintillation counters commonly used in beam instrumentation and represent proof-of-work for an automated particle counting system. The algorithms were trained on a labelled set of over 150 000 experimental particle data. The results of the three classification approaches were compared to find a solution that best mitigates the effects of particle pile-ups. The two best-achieving algorithms were the CNN and PPCA, achieving an accuracy of 99.8\%. This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730.
	Poster WEP42 [1.370 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP42
About •	Received ※ 11 September 2022 — Revised ※ 25 October 2022 — Accepted ※ 01 December 2022 — Issue date ※ 08 December 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOP10

Removing Noise in BPM Measurements with Variational Autoencoders

operation, optics, coupling, controls

43

J.P. Edelen, J.A. Einstein-Curtis, C.C. Hall, M.J. Henderson
RadiaSoft LLC, Boulder, Colorado, USA
A.L. Romanov
Fermilab, Batavia, Illinois, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699.
Noise in beam measurements is an ever-present challenge in accelerator operations. In addition to the challenges presented by hardware and signal processing, new operational regimes, such as ultra-short bunches, create additional difficulties in routine beam measurements. Techniques in machine learning have been successfully applied in other domains to overcome challenges inherent in noisy data. Variational autoencoders (VAEs) are shown to be capable of removing significant leevels of noise. A VAE can be used as a pre-processing tool for noise removal before the de-noised data is analyzed via other methods, or the VAE can be directly used to make beam dynamics measurements. Here we present the use of VAEs as a tool for addressing noise in BPM measurements.

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP10

About •

Received ※ 29 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 24 November 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOP38

Beam Profile Monitoring and Distributed Analysis Using the RabbitMQ Message Broker

interface, software, controls, Ethernet

140

D. Proft, K. Desch, D. Elsner, F. Frommberger, S. Kronenberg, A. Spreitzer, M.T. Switka
ELSA, Bonn, Germany

The ELSA facility utilizes several digital cameras for beam profile measurements on luminous screens and synchrotron radiation monitors. Currently a multitude of devices with analog signal output are being replaced in favor of digital outputs, preferably with data transfer via Ethernet. The increased network traffic for streaming, analyzing, and distribution of processed data to control system and machine operators is managed through a supplementary camera network in which distributed computing is performed by the RabbitMQ message broker. This allows performant and platform-independent image acquisition from multiple cameras, real time profile analysis, and supports programming interfaces for C++ and Python. The setup and performance of the implementation are presented.

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP38

About •

Received ※ 07 September 2022 — Revised ※ 09 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 28 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOP43

Web-Based Application for Cable Simulation Models

simulation, instrumentation, electron, impedance

156

M.C. Paniccia, S.L. Clark, D.M. Gassner, R.L. Hulsart, P. Thieberger
BNL, Upton, New York, USA

Attenuation in a lossy coaxial cable increases over distance and varies over frequency. Having a model of these variations can help predict the expected loss and distortion of a signal. This paper discusses a free web-based application developed to provide accurate SPICE models for various coaxial cable types. The user can specify a length and select between different cable types, or upload their own cable attenuation curve, and receive a SPICE model for that cable. These simulation models have been used to assist the design and development of new instrumentation systems for the future Electron Ion Collider (EIC).

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP43

About •

Received ※ 06 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 13 September 2022 — Issue date ※ 22 November 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP38

Deep Neural Network for Beam Profile Classification in Synchrotron

diagnostics, operation, emittance, synchrotron

323

M. Piekarski
NSRC SOLARIS, Kraków, Poland

Funding: The presented work has been achieved in collaboration with AGH University of Science and Technology in Kraków as a part of a PhD thesis.
The main goal of NSRC SOLARIS is to provide scientific community with high quality synchrotron light. To achieve this, it is necessary to constantly monitor many subsystems responsible for beam stability and to analyze data about the beam itself from various diagnostic beamlines. In this work a deep neural network for transverse beam profile classification is proposed. Main task of the system is to automatically assess and classify transverse beam profiles based solely on the evaluation of the beam image from the Pinhole diagnostic beamline at SOLARIS. At the present stage, a binary assignment of each profile is performed: stable beam operation or unstable beam operation / no beam. Base model architecture consists of a pre-trained convolutional neural network followed by a densely-connected classifier and the system reaches accuracy at the level of 90%. The model and the results obtained so far are discussed, along with plans for future development.

Poster TUP38 [0.376 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP38

About •

Received ※ 30 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 15 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP39

Neural Network Inverse Models for Implicit Optics Tuning in the AGS to RHIC Transfer Line

quadrupole, diagnostics, operation, controls

327

J.P. Edelen, N.M. Cook, J.A. Einstein-Curtis
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Brown, V. Schoefer
BNL, Upton, New York, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682
One of the fundamental challenges of using machine-learning-based inverse models for optics tuning in accelerators, particularly transfer lines, is the degenerate nature of the magnet settings and beam envelope functions. Moreover, it is challenging, if not impossible, to train a neural network to compute correct quadrupole settings from a given set of measurements due to the limited number of diagnostics available in operational beamlines. However, models that relate BPM readings to corrector settings are more forgiving, and have seen significant success as a benchmark for machine learning inverse models. We recently demonstrated that when comparing predicted corrector settings to actual corrector settings from a BPM inverse model, the model error can be related to errors in quadrupole settings. In this paper, we expand on that effort by incorporating inverse model errors as an optimization tool to correct for optics errors in a beamline. We present a toy model using a FODO lattice and then demonstrate the use of this technique for optics corrections in the AGS to RHIC transfer line at BNL.

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP39

About •

Received ※ 05 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 12 November 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP42

Fast Orbit Feedback Upgrade at SOLEIL

interface, FPGA, controls, electron

339

R. Broucquart, N. Hubert
SOLEIL, Gif-sur-Yvette, France

In the framework of the SOLEIL II project, the diagnostics group must anticipate ahead of the dark period the upgrade of important system like the BPM electronics, the timing system end the Fast Orbit Feedback (FOFB). The FOFB is a complex system that is currently embedded in the BPM electronics modules (eBPM). A new flexible stand-alone platform is under conception to follow the future upgrades of surrounding equipment, and to allow the integration of future correction schemes. In this paper we will present the current status of technical decisions, tests and developments.

Poster TUP42 [3.305 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP42

About •

Received ※ 07 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 25 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEP40

A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation

Ethernet, software, instrumentation, data-acquisition

500

R.R. Santucci, J.S. Diamond, N. Eddy, A. Semenov, D.C. Voy
Fermilab, Batavia, Illinois, USA

The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1GbE and aggregated at a switch which up linked to a 10GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10Gbit/s using off the shelf Ethernet technology.

Poster WEP40 [0.738 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP40

About •

Received ※ 08 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 04 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEP42

Application of Machine Learning towards Particle Counting and Identification

Windows, extraction, detector, experiment

508

S.E. Engel
University of Essex, Physics Centre, Colchester, United Kingdom
P. Boutachkov, R. Singh
GSI, Darmstadt, Germany

An exploration into the application of three machine learning (ML) approaches to identify and separate events in the detectors used for particle counting at the GSI Helmholtz Centre for Heavy Ion Research. A convolutional neural network (CNN), a shape-based template matching algorithm (STMF) and Peak Property-based Counting Algorithm (PPCA) were developed to accurately count the number of particles without domain-specific knowledge required to run the currently used algorithm. The three domain-agnostic ML algorithms are based on data from scintillation counters commonly used in beam instrumentation and represent proof-of-work for an automated particle counting system. The algorithms were trained on a labelled set of over 150 000 experimental particle data. The results of the three classification approaches were compared to find a solution that best mitigates the effects of particle pile-ups. The two best-achieving algorithms were the CNN and PPCA, achieving an accuracy of 99.8\%.
This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730.

Poster WEP42 [1.370 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP42

About •

Received ※ 11 September 2022 — Revised ※ 25 October 2022 — Accepted ※ 01 December 2022 — Issue date ※ 08 December 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)