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This project has received funding from the European Unions Horizon 2020 Research and Innovation programme under Grant Agreement No 730871. Comprehensive simulations of distortions and Comprehensive simulations of distortions and their

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This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No 730871.

Comprehensive simulations of distortions and Comprehensive simulations of distortions and their mitigations related to measurements their mitigations related to measurements with ionisation profile monitors with ionisation profile monitors

1st ARIES Annual Meeting 1st ARIES Annual Meeting Riga Tech. Univ., Latvia Riga Tech. Univ., Latvia May 24th, 2018 May 24th, 2018 Dominik Vilsmeier / GSI Dominik Vilsmeier / GSI

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Preface Preface

  • M. Sapinski et al 2012 "The first experience with LHC

Beam Gas Ionization Monitor" Proc. IBIC2012

Beam Gas Ionization Monitor installed at LHC (CERN) Profile broadening compared to Wire Scanner data

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Profile broadening - why? Profile broadening - why?

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Profile broadening - why? Profile broadening - why?

Effects related to MCP Optical point-spread function Distortion of electron trajectories due to beam space charge Distortion due to electron gyroradius

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Simulations Simulations

PyECLOUD (electron cloud studies)

Available

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Simulations Simulations

PyECLOUD (electron cloud studies) PyECLOUD-BGI (electron movement in IPM)

Available

Adaption for IPM

Upgrade

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Workshop @ CERN | 2016 Workshop @ CERN | 2016

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Workshop @ CERN | 2016 Workshop @ CERN | 2016

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Workshop @ CERN | 2016 Workshop @ CERN | 2016

Design of electrodes for new devices Simulation of field configuration for removing electron cloud from the device volume Simulation of profile distortion

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Workshop @ CERN | 2016 Workshop @ CERN | 2016

Design of electrodes for new devices Simulation of field configuration for removing electron cloud from the device volume Simulation of profile distortion Simulation code involving ionization and transport of electrons in realistic fields Code base with core developers Infrastructure for code maintenance and user support

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Simulation codes Simulation codes

Various programs / scripts: different applications (LINAC, Synchrotron, ...) different solutions

Combine the effort and create a common tool

→ suitable for all different use cases

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Code modularity Code modularity

Simulation

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Code modularity Code modularity

Simulation Initial velocities

Single and double ionization cross sections Gas jet velocity

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Code modularity Code modularity

Simulation Initial velocities Guiding fields

Single and double ionization cross sections Gas jet velocity Uniform fields Two and three dim. CST field maps

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Code modularity Code modularity

Simulation Initial velocities Beam fields Guiding fields

Single and double ionization cross sections Gas jet velocity Uniform fields Two and three dim. CST field maps Analytic formulations Numerical solvers Both two and three dim.

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Code modularity Code modularity

Simulation Initial velocities Beam fields Guiding fields Particle tracking

Single and double ionization cross sections Gas jet velocity Uniform fields Two and three dim. CST field maps Analytic formulations Numerical solvers Both two and three dim.

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Varying in computational complexity as well as numerical accuracy

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Workshop @ GSI | 2017 Workshop @ GSI | 2017

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Sponsored by

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Workshop @ GSI | 2017 Workshop @ GSI | 2017

33 participants 13 institues 9 countries

BINP BINP

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Sponsored by

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Workshop @ GSI | 2017 Workshop @ GSI | 2017

Compilation of IPM realization Operational issues Simulation code innovation

Sponsored by

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Workshop @ GSI | 2017 Workshop @ GSI | 2017

Compilation of IPM realization Operational issues Simulation code innovation Present the new simulation tool Collect feedback from the community

Sponsored by

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Workshop @ GSI | 2017 Workshop @ GSI | 2017

Compilation of IPM realization Operational issues Simulation code innovation Discuss new use cases and applications Investigate corresponding models Present the new simulation tool Collect feedback from the community

Sponsored by

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Workshop @ GSI | 2017 Workshop @ GSI | 2017

Compilation of IPM realization Operational issues Simulation code innovation Benchmark simulation against each other Perform measurements for comparison with data Discuss new use cases and applications Investigate corresponding models Present the new simulation tool Collect feedback from the community

Sponsored by

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https://pypi.org/project/virtual-ipm https://gitlab.com/IPMsim/Virtual-IPM

Virtual-IPM Virtual-IPM

Emphasis on flexibility and extensibility Covers a broad range of use cases Acts as framework as well as stand-alone application Suitable for developers and users

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https://pypi.org/project/virtual-ipm https://gitlab.com/IPMsim/Virtual-IPM

Virtual-IPM Virtual-IPM

Emphasis on flexibility and extensibility Covers a broad range of use cases Acts as framework as well as stand-alone application Suitable for developers and users

$ pip install virtual-ipm # Easy installation

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Graphical User Interface Graphical User Interface

<?xml version="1.0" ?> <Virtual-IPM version="1.2.2"> <Beams> <Beam> <Parameters> <Energy unit="TeV">6.5</Energy> <BunchPopulation>2.1e+11</BunchPopulation> <ParticleType> <ChargeNumber>1</ChargeNumber> <RestEnergy unit="MeV"> %(proton mass energy equivalent in MeV) </RestEnergy> </ParticleType> </Parameters>

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Graphical User Interface Graphical User Interface

<?xml version="1.0" ?> <Virtual-IPM version="1.2.2"> <Beams> <Beam> <Parameters> <Energy unit="TeV">6.5</Energy> <BunchPopulation>2.1e+11</BunchPopulation> <ParticleType> <ChargeNumber>1</ChargeNumber> <RestEnergy unit="MeV"> %(proton mass energy equivalent in MeV) </RestEnergy> </ParticleType> </Parameters>

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Use cases Use cases

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Use cases Use cases

IPM Profile distortion IPM Profile distortion

Guiding field non-uniformities Beam space-charge interaction Influence of ionization momenta IPM Profile "rectification", e.g. through device upgrades or software based solutions

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Use cases Use cases

IPM Profile distortion IPM Profile distortion

Guiding field non-uniformities Beam space-charge interaction Influence of ionization momenta IPM Profile "rectification", e.g. through device upgrades or software based solutions

IPM Design IPM Design

Field cage design Magnetic field strength required for suppressing distortions

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Use cases Use cases

IPM Profile distortion IPM Profile distortion

Guiding field non-uniformities Beam space-charge interaction Influence of ionization momenta IPM Profile "rectification", e.g. through device upgrades or software based solutions

IPM Design IPM Design

Field cage design Magnetic field strength required for suppressing distortions

BIF space-charge effects BIF space-charge effects

Excited ions move under the influence of beam fields Gas jet velocity is retained by ions

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PS IPM Design PS IPM Design

Profile distortion cannot be sufficiently suppressed by electric field

Energy 25 GeV Bunch pop. 1.33e11 Bunch size 3.7 x 1.4 mm

  • length (4σ)

3.0 ns

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PS IPM Design PS IPM Design

Profile distortion cannot be sufficiently suppressed by electric field Confirming the requirement for a magnetic guiding field

Energy 25 GeV Bunch pop. 1.33e11 Bunch size 3.7 x 1.4 mm

  • length (4σ)

3.0 ns

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LHC Profile distortion LHC Profile distortion

Beam Energy 6.5 TeV Bunch pop. 2.1e11 Bunch size 270 x 360 μm

  • length (4σ)

0.9 ns IPM Electrode dist. 85 mm Applied voltage 4 kV Magnetic field 0.2 T

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LHC Profile distortion LHC Profile distortion

Gyro-velocity oscillates due to ExB-drift Electrons end up with increased velocity

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LHC Profile distortion LHC Profile distortion

Gyro-velocity oscillates due to ExB-drift Electrons end up with increased velocity Displacement due to ... ... ionization (Δx1) ... space-charge (Δx2) ... gyro-motion (Δx3)

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SPS Profile distortion SPS Profile distortion

Energy 450 GeV Bunch pop. 2.86e11 Bunch size 835 x 451 μm

  • length (4σ)

1.6 ns

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SPS Profile distortion SPS Profile distortion

Measurement at 16 mT Convoluted with 520 μm point-spread function

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BIF for e-Lens @ HL-LHC BIF for e-Lens @ HL-LHC

Vacuum gauge N fluorescent gas

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B e a m

Viewport Lens, Image- Intensifier, Camera

BIF schematic

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

BIF for e-Lens @ HL-LHC BIF for e-Lens @ HL-LHC

Vacuum gauge

B e a m

Viewport Lens, Image- Intensifier, Camera

BIF schematic

N gas jet

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

BIF for e-Lens @ HL-LHC BIF for e-Lens @ HL-LHC

Simulation of complex setup for diagnostics based on BIF with supersonic gas jet Excited ions interact with beam fields and suffer from a displacement until they decay

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Profile reconstruction Profile reconstruction

Machine Learning Machine Learning

... can be used for establishing a relationship between measured profiles and beam profiles. E.g. deep learning E.g. deep learning

Measured profile Beam profile

Beam pars.

This relationship is inferred using data

  • btained from IPM simulations.

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Profile reconstruction Profile reconstruction

Application of Machine Learning for deducing the relation between measured (distorted) and original beam profiles

Sweep the relevant parameter space and use simulation data for training Parameter Range Bunch pop. [1e11] 1.1 – 2.1 ppb Bunch width (1σ) 270 – 370 μm Bunch height (1σ) 360 – 600 μm Bunch length (4σ) 0.9 – 1.2 ns

Very good results for testing with simulation data → first tests with measurement data are planned

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Conclusions Conclusions

Lots of advancement in the field of IPM simulations The efforts were combined into a common and generic simulation tool

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Conclusions Conclusions

Lots of advancement in the field of IPM simulations The efforts were combined into a common and generic simulation tool Allows for simulating a broad range of cases, including LINACs, Synchrotrons and

  • ther beam

instruments such as BIF monitors

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1st ARIES Annual Meeting, May 24th 2018, D. Vilsmeier

Conclusions Conclusions

Lots of advancement in the field of IPM simulations The efforts were combined into a common and generic simulation tool Allows for simulating a broad range of cases, including LINACs, Synchrotrons and

  • ther beam

instruments such as BIF monitors Simulations are not

  • nly useful concerning

IPM design but can also be used for software-based profile reconstruction (work in progress)

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Conclusions Conclusions

https:/ /ipmsim.gitlab.io/IPMSim https:/ /ipmsim.gitlab.io/IPMSim Lots of advancement in the field of IPM simulations The efforts were combined into a common and generic simulation tool Allows for simulating a broad range of cases, including LINACs, Synchrotrons and

  • ther beam

instruments such as BIF monitors Simulations are not

  • nly useful concerning

IPM design but can also be used for software-based profile reconstruction (work in progress) The community efforts have led to an established collaboration concerned about topics related to simulations of IPMs and similar beam instruments

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Realization of the various tasks was possible thanks to the active participation of: Peter Forck Mariusz Sapinski Kenichirou Satou

(1) (1) (2)

Rahul Singh James Storey Serban Udrea

(1) (3) (1)

GSI J-PARC/KEK CERN

(1) (2) (3)

Thank you Thank you

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