Touschek#191
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`compt_scale` is a multiplicative factor applied to the total Compton cross-section. Increasing this factor scales up photon generation in the radiative Bhabha model. This can be useful for testing or for producing a larger number of radiative Bhabha scattering events.
When a line is generated through MAD-X sequence conversion, the line end up having markers sequencename$start and sequencename$end. In the line table these are present, but they do not have an associated s position and this causes an error!
Prevented sampling of particles with |δ|>LMA in Touschek MC by reducing the
longitudinal cutoff per element where needed:
nz_eff = min(nz, 0.9 * min(|δN|, δP) / σδ)
This ensures sampled δ stays strictly within the LMA,
avoiding pathological small-angle events with huge weights that distorted
RMC/RP and Touschek lifetime estimates.
- Adjustment is local: only elements with tight acceptance are clamped
- Prints a warning when nz_eff < nz
- Restores stable RMC/RP≈1 and meaningful lifetime results
giadarol
requested changes
Apr 29, 2026
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| touschek_manager = xf.TouschekManager( | ||
| line, | ||
| momentum_aperture=df_momentum_aperture, |
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Let's say that all our API takes tables and not Pandas dataframe
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| touschek_manager.initialise_touschek() | ||
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| touschek_elements = tab.rows[tab.element_type == 'TouschekScattering'].name | ||
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| line.discard_tracker() | ||
| line.build_tracker(_context=xo.ContextCpu(omp_num_threads='auto')) | ||
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| particles_list = [] | ||
| for ii in range(len(touschek_elements)): | ||
| element = touschek_elements[ii] # xf.TouschekScattering | ||
| s_start_elem = tab.rows[tab.name == element].s[0] | ||
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| # Touschek! | ||
| particles = line[element].scatter() | ||
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| # Track! | ||
| print(f"\nTrack particles scattered at {element} at s = {s_start_elem}") | ||
| line.track(particles, ele_start=element, ele_stop=element, num_turns=nturns, with_progress=1) | ||
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| particles_list.append(particles) | ||
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| particles = xt.Particles.merge(particles_list) |
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Explain with comments what different steps are doing
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no need, lets' put the acknowledgment in the file
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once merged this should go in the Xsuite docs
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| If you publish results obtained with this routine, please cite: | ||
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| - M. Borland, “elegant: A Flexible SDDS-Compliant Code for Accelerator Simulation,” APS LS-287 (2000). | ||
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| - A. Xiao and M. Borland, “Monte Carlo simulation of Touschek effect,” *Phys. Rev. ST Accel. Beams* **13**, 074201 (2010). | ||
| DOI: 10.1103/PhysRevSTAB.13.074201 |
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Put these references in the Python docstring so that they are visible in the documentation
| # Include the license file | ||
| # Include the license files | ||
| include LICENSE.txt | ||
| recursive-include xfields/third_party *LICENSE* |
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| recursive-include xfields *.h | ||
| recursive-include xfields *.clh | ||
| recursive-include xfields *.clh No newline at end of file |
giadarol
reviewed
Apr 29, 2026
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Please add a Touscheck chapter in Physics Guide
| CLASSICAL_ELECTRON_RADIUS = physical_constants['classical electron radius'][0] | ||
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| class TouschekCalculator: | ||
| def __init__(self, manager): |
| break | ||
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| class TouschekManager: |
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Description
This PR introduces the possibility to perform Monte Carlo simulations of Touschek scattering in xfields.
It provides:
a new
TouschekScatteringbeam element implementing a Monte Carlo Touschek scattering kernel,a
TouschekManagerthat configures Touschek scattering centers using optics and a supplied local momentum aperture profile,a
TouschekCalculatorthat evaluates Piwinski Touschek scattering rates and integrated per-section rates, used to correctly weight scattered macroparticles.This enables end-to-end Touschek studies within Xsuite, including generation of Touschek-scattered particles, tracking, Touschek loss maps analysis, and Touschek lifetime evaluations.
Checklist
Mandatory:
Optional: