Electron cloud effects on the LHC beam dynamics
A. Romano, G. Iadarola, G. Rumolo
Many thanks to: D. Cesini, P. Dijkstal, K. Li, E. Mètral, M. Schenk and INFN-CNAF
institute in Bologna
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Outline
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Basics on the electron cloud in particle accelerators
 Electron cloud buildup and main effects on the beam dynamics
•
Simulation studies
 Observations in the LHC vs simulation results
•
Conclusions
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Electron cloud build up
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Primary (seed) electrons are generated inside beam chamber (gas ionization, photoemission)
•
Seed electrons are accelerated by beam field and produce secondary electrons when hitting
the wall
•
If the Secondary Electron Yield (SEY) of the surface is large enough, it can drive an avalanche
electron production  exponential growth of electron density (multipacting regime)
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Electron cloud build up
•
Primary (seed) electrons are generated inside beam chamber (gas ionization, photoemission)
•
Seed electrons are accelerated by beam field and produce secondary electrons when hitting
the wall
•
If the Secondary Electron Yield (SEY) of the surface is large enough, it can drive an avalanche
electron production  exponential growth of electron density (multipacting regime)
•
Electron distribution within the chamber is strongly influenced by the magnetic field
LHC Arc Dipole
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LHC Arc Quadrupole
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Electron cloud effects
The presence of an EC inside an accelerator ring can be revealed by several typical signatures
 Machine observables
•
Heat load on the chamber’s walls
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Vacuum degradation
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Fast pressure rise and outgassing
 Beam observables
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Transverse instabilities & emittance growth
•
Tune shift & spread
•
Incoherent beam losses
Understanding of beam observables relies on PyECLOUD-PyHEADTAIL simulations
• Simulations are very demanding in terms of time and computational resources 
typical study requires hundreds of CPUs (8 CPUs per job) and 3-4 week to simulate 104
turns (INFN-CNAF clusters used for this purpose)
• Recent work has been focused on increasing the performance of our simulation tools (1)
(1) G. Iadarola et al, “Evolution of python tools for the simulation of electron cloud effects”, in Proceedings of the 8th International
Particle Accelerator Conference, Copenhagen, Denmark, THPAB043 (2017)
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Simulation studies
An extensive simulation campaign has been carried out in order to improve the understanding
of machine settings on the instability induced by the EC  very long simulation run needed to
approach the time scale of the observed instabilities
Main effects studied (1)
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EC in the main LHC arcs (dipoles and quadrupoles)
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Beam energy
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Different machine settings (chromaticity, octupoles and transverse feedback)
Vert Chromaticity
Beam energy effect
Chromaticity effect
(1) A. Romano et al., “Electron cloud induced instabilities in the LHC”, presentation at the Joint Ecloud-PyHEADTAIL Meeting, 12 May
2017, https://indico.cern.ch/event/638087/ (2017)
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Observations vs simulation results
2015 (after scrubbing run): high chromaticity and octupoles settings, together with the full
performance of the damper, were needed to ensure the beam stability at 450 GeV
Beam lifetime measured for different settings of
vertical tune and chromaticity (1)
• Drop observed after increasing Q’V
• Lifetime recovered by lowering the tune
Tune footprints as obtained from PyECLOUDPyHEADTAIL simulations for different tunes (2)
• Large tune spreads due to high settings
• Lower tunes needed to avoid Qy = .33 
lifetime improved
Design fractional tunes (0.28,0.31)
2016 operational tunes (0.27,0.295)
Simulation settings:
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EC in the arcs
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Q’H,V = 15/15
•
Oct current = 26 A
(1) A. Romano et al., “Effect of the electron cloud on the tune footprint at 450 GeV”, presentation at the LBOC Meeting No 51, 27
October 2015, https://indico.cern.ch/event/455596/contributions/1966373/ (2015)
(2) A. Romano et al., “Macroparticle simulation studies of the LHC beam dynamics in the presence of the electron cloud”, in
Proceedings of the 8th International Particle Accelerator Conference, Copenhagen, Denmark, TUPVA018 (2017)
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Observations vs simulation results
2016 (beginning of the run): In spite of high machine settings, instabilities were observed
at 6.5 TeV during collisions (stable beams) (1)
Vertical emittance blown up during collision
• Affecting only bunches at tail of trains
• Instabilities occurred for bunch intensities
between 0.7e11 and 1.0e11
EC central density (from PyECLOUD) vs bunch
intensity
• for lower beam intensity, the EC density
can become sufficiently high to drive an
instability
EC in
dipoles?
(1) A. Romano et al., “Instabilities in stable beams”, presentation at the ½ -day internal review of LHC performance limitations
during run 2, 29 November 2016, https://indico.cern.ch/event/589625/ (2016)
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Observations vs simulation results
2016: Tests were performed at injection energy in order to assess the EC impact on the
beam stability and potential mitigation strategies
Comparison of emittance measurements at 450GeV
for two test fills : EC free pattern and standard 25ns
Simulation of transverse instabilities due to the EC
 Any sizable
emittance
blowup observed
 High machine
settings needed to
reach a good beam
quality
Can we explain this horizontal emittance blowup?
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EC in quadrupoles alone can explain why
instabilities can be seen in both planes
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Observations vs simulation results
2016: Tests were performed at injection energy in order to assess the EC impact on the
beam stability and potential mitigation strategies
Comparison of emittance measurements at 450GeV
for two test fills : EC free pattern and standard 25ns
Simulation of transverse instabilities due to the EC
 Any sizable
emittance
blowupingredient
observed to explain observed LHC instabilities  detailed
Quads are found to be a critical
studies have been carried out in order to investigate the effect of different machine settings
(chromaticity, octupoles and bunch-by-bunch transverse feedback) on the beam dynamics [1]
 High machine
settings needed to
reach a good beam
quality
Can we explain this horizontal emittance blowup?
EC in quadrupoles alone can explain why
instabilities can be seen in both planes
[1] A. Romano et al., “Electron cloud induced instabilities in the LHC”, presentation at the Joint Ecloud-PyHEADTAIL Meeting, 12 May
2017, https://indico.cern.ch/event/638087/ (2017)
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Conclusions
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Electron cloud could pose important challenges to the machine operation  difficulty to
ensure the beam stability and a good beam quality from injection to collision
•
Improvements of simulation tools were needed to exploit new scenarios
•
Several configurations (EC in dipoles and quadrupoles, chromaticity, octupoles, injection
energy, flattop, transverse feedback) have been simulated in order to explain the
underlying mechanism of the observed instabilities in the LHC and find potential
mitigation strategies
•
Simulation results could explain several machine observations
 Poor lifetime with the nominal tunes at injection
 Instability in stable beams for lower than nominal bunch intensity
 Horizontal instabilities at injection (found to be driven by quadrupoles)
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Thanks for your attention!
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Electron cloud induced transverse instability
Electron density during a bunch passage
PyECLOUD-PyHEADTAIL simulation
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e-cloud driven instability
PyECLOUD-PyHEADTAIL simulation
Electrons are attracted by the circulating proton bunch and ”fly” through it resulting in
regions of high electron density within the bunch itself  transverse instability
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PyECLOUD-PyHEADTAIL simulations
The beam dynamics is modeled using PyHEADTAIL(2)
1. The ring is split into a set of segments (IPs)
2. Linear periodic maps is used for transverse tracking from one IP to the next
3. Synchrotron motion is applied once per turn
4. At each IP the bunch/e-cloud interaction takes place
IPs
more than 104 simulated
turns are needed ….
Segment with
linear transport
(2)
K.Li et al.,“Code Development for Collective Effects”, HB16, Malmo, Sweden, paper WEAM3X01, 2016
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PyECLOUD-PyHEADTAIL simulations
The beam dynamics is modeled using PyHEADTAIL(2)
1. The ring is split into a set of segments (IPs)
2. Linear periodic maps is used for transverse tracking from one IP to the next
3. Synchrotron motion is applied once per turn
4. At each IP the bunch/e-cloud interaction takes place
New approach: PyECLOUD(3)
used in combination with PyHEADTAIL by means of an ad-hoc developed interface
(2)
(3)
K.Li et al.,“Code Development for Collective Effects”, HB16), Malmo, Sweden, paper WEAM3X01, 2016
G.Iadarola, "Electron cloud studies for CERN particle accelerators and simulation code development", CERN-THESIS 2014-047
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