Synchrotron radiation
Twiss and track with radiation
The following example illustrates the use of the synchrotron radiation in Xsuite.
Explanations can be found in the comments interleaved in the code. For the
considered case, the lattice is loaded from a MAD-X thick sequence and transformed
in thin using the MAKETHIN
command of MAD-X to obtain a thin sequence compatible
with Xsuite.
See also: xtrack.Line.configure_radiation()
import time
import numpy as np
from cpymad.madx import Madx
import xtrack as xt
# Import a thick sequence
mad = Madx()
mad.call('../../test_data/clic_dr/sequence.madx')
mad.use('ring')
# Makethin
mad.input(f'''
select, flag=MAKETHIN, SLICE=4, thick=false;
select, flag=MAKETHIN, pattern=wig, slice=1;
select, flag=makethin, class=rfcavity, slice=1;
MAKETHIN, SEQUENCE=ring, MAKEDIPEDGE=true;
use, sequence=RING;
''')
mad.use('ring')
# Build xtrack line
print('Build xtrack line...')
line = xt.Line.from_madx_sequence(mad.sequence['RING'])
line.particle_ref = xt.Particles(
mass0=xt.ELECTRON_MASS_EV,
q0=-1,
gamma0=mad.sequence.ring.beam.gamma)
# Build tracker
print('Build tracker ...')
line.build_tracker()
################################
# Enable synchrotron radiation #
################################
# we choose the `mean` mode in which the mean power loss is applied without
# stochastic fluctuations (quantum excitation).
line.configure_radiation(model='mean')
#########
# Twiss #
#########
tw = line.twiss(eneloss_and_damping=True)
# By setting `eneloss_and_damping=True` we can get additional information
# from the twiss for example:
# - tw['eneloss_turn'] provides the energy loss per turn (in eV).
# - tw['damping_constants_s'] provides the damping constants in x, y and zeta.
# - tw['partition_numbers'] provides the corresponding damping partion numbers.
# - tw['eq_nemitt_x'] provides the equilibrium horizontal emittance.
# - tw['eq_nemitt_y'] provides the equilibrium vertical emittance.
# - tw['eq_nemitt_zeta'] provides the equilibrium longitudinal emittance.
############################################
# Generate particles and track (mean mode) #
############################################
# Build three particles (with action in x,y and zeta respectively)
part_co = tw['particle_on_co']
particles = line.build_particles(
x_norm=[500., 0, 0], y_norm=[0, 500, 0], zeta=part_co.zeta[0],
delta=np.array([0,0,1e-2]) + part_co.delta[0],
nemitt_x=1e-9, nemitt_y=1e-9)
# Save initial state
particles_0 = particles.copy()
# Track
num_turns = 5000
line.track(particles, num_turns=num_turns, turn_by_turn_monitor=True)
# Save monitor
mon_mean_mode = line.record_last_track
############################
# Switch to `quantum` mode #
############################
# We switch to the `quantum` mode in which the power loss from radiation is
# applied including stochastic fluctuations (quantum excitation).
# IMPORTANT: Note that this mode should not be used to compute twiss parameters
# nor to match particle distributions. For this reason we switch
# to quantum mode only after having generated the particles.
line.configure_radiation(model='quantum')
# We reuse the initial state saved before
particles = particles_0.copy()
num_turns = 5000
line.track(particles, num_turns=num_turns, turn_by_turn_monitor=True)
mon_quantum_mode = line.record_last_track
import matplotlib.pyplot as plt
plt.close('all')
figs = []
for ii, mon in enumerate([mon_mean_mode, mon_quantum_mode]):
fig = plt.figure(ii + 1)
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312, sharex=ax1)
ax3 = fig.add_subplot(313, sharex=ax1)
ax1.plot(1e3*mon.x[0, :].T)
ax2.plot(1e3*mon.y[1, :].T)
ax3.plot(1e3*mon.delta[2, :].T)
i_turn = np.arange(num_turns)
ax1.plot(1e3*(part_co.x[0]
+(mon.x[0,0]-part_co.x[0])*np.exp(-i_turn*tw['damping_constants_turns'][0])))
ax2.plot(1e3*(part_co.y[0]
+(mon.y[1,0]-part_co.y[0])*np.exp(-i_turn*tw['damping_constants_turns'][1])))
ax3.plot(1e3*(part_co.delta[0]
+(mon.delta[2,0]-part_co.delta[0])*np.exp(-i_turn*tw['damping_constants_turns'][2])))
ax1.set_ylabel('x [mm]')
ax2.set_ylabel('y [mm]')
ax3.set_ylabel('delta [-]')
ax3.set_xlabel('Turn')
plt.show()
# Complete source: xtrack/examples/radiation/005_radiation_example.py
Compensate radiation energy loss (tapering)
Xtrack Line provides a method to compensate the energy loss due to the synchrotron radiation. This is done by configuring the phase of the RF cavities to compensate for the energy loss, and adapting the strength of the magnets to the local momentum of the particle on the closed orbit. This is illustrated in the following example.
See also: xtrack.Line.compensate_radiation_energy_loss()
import json
import numpy as np
import xtrack as xt
#########################################
# Load line and twiss with no radiation #
#########################################
filename = '../../test_data/clic_dr/line_for_taper.json'
with open(filename, 'r') as f:
line = xt.Line.from_dict(json.load(f))
line.build_tracker()
line.configure_radiation(model=None)
tw_no_rad = line.twiss(method='4d', freeze_longitudinal=True)
###############################################
# Enable radiation and compensate energy loss #
###############################################
line.configure_radiation(model='mean')
# - Set cavity lags to compensate energy loss
# - Taper magnet strengths to avoid optics and orbit distortions
line.compensate_radiation_energy_loss()
##############################
# Twiss to check the results #
##############################
tw = line.twiss(method='6d')
tw.delta # contains the momentum deviation along the ring
# Complete source: xtrack/examples/tapering/000_taper.py