Introduction to Xsuite

Giovanni Iadarola

• Xsuite begun at the beginning of 2021

Motivation

• There were many independently developed multi-particle tracking softwares with different features
• The learning curves are long and specific
• Difficult to define a consistent strategy with future challenges - such as FCC, muon collider
• Opted to start with a new design considering all requirements
• No need to start from scratch - reuse experience and sometimes source code

Requirements

• Sustainable
  • The community is relatively small
  • Need something easy to maintain and develop
  • Favoured mainstream technology: python
  • Keep the code simple and slim: student friendly
• Easy and flexible to use: scriptable
• Speed matters
  • Maintain performance with sixtrack on CPU and sixtracklib on GPU
• Provide GPU interface

Structure

• A set of python packages
• Already used in BE-ABP
• Newcomers are often aware of python, a short learning curve
• Python is easily extended with C, C++, and FORTRAN
• Support GPUs, as some simulations can only be done on GPUs
• Complicated market situation
  • No accepted standard
• Multiplatform code: support multiple vendors
• New standards are already expected
• Open source GPU packages leveraged

Development

• Demonstrated a short learning curve
• Already used in production for some studies
• Users encouraged to contribute with code, tests, documentation etc

Collective Effects

• Xsuite can handle collective elements - impedance, space-charge etc
• The code is aware of these elements, where the action on one particle depends on the coordinates on the other
• No special action required by the user - the code automatically handles asynchronicity

Interface

• Built to be as extensible as possible
• Any element that has a tracking method is a valid element for Xtrack
• In particular, PyHEADTAIL elements are valid - but currently need a compatibility mode

Comparison and Advanced Features

• Extremely small errors when checking against SixTrack (machine precision)
• Similar compilation time to SixTrack
• Already used in Dynamic Aperture studies (with a fully pythonic approach)
• Use of ML for smart sampling of phase space
• Feedback: Xsuite was fundamental for these projects

How it works

• Searches 6D closed orbit
• First order turn matrix
• Eigenvector tracking of particles
• Compute twiss parameters from eigenvalues (bonus)
• Repeat off-momentum for chromaticity
• Tested for HL-LHC, PSB, ELENA, Elettra, CLIC-DR, FCC-ee

Xdeps

• Deferred expressions from MAD-X
• Knobs imported from MAD-X can be easily changed before the simulation or during the simulation
• Variables can also be inspected - very powerful

Synchrotron Radiation

• Not available in SixTrack
• Full quantum description
• Xtrack twiss computes energy loss

Xcoll

• Installs collimators in Xsuite beamlines
• Configures the gaps based on user configuratoin
• Different particle-matter simulation engines
  • K2 ported from sixtrack
  • Geant4-BDSIM tested in full loss-map studies
  • FLUKA
• More modules for precisely locating particle loss
• still not implemented

Experience

• Modular code is feasible
• Different computing platforms can be supported under the same codebase
• Gently moving towards production

Questions

• Pulling in geometry from CAD files is not currently available
  • This can be done with FLUKA
• Does the code interface or link with many existing libraries?
  • Single particle: ported from SixTrackLib directly
  • Space charge: written from scratch, but inspired by PyHEADTAIL
  • Collected effects: interface with PyHEADTAIL
• Elements: thintracking, so in the end everything is a multipole
• Sequences can be imported from MAD-X
  • It is imported from cpymad sequence object
• Any disadvantages with MAD-X?
  • MAD-X is limited with space charge
  • No real disadvantages
  • MAD-X might take longer to import files
  • Do the import once, and export
• MAD-X imports - do deferred expressions impact performance
  • We do pull, not push
  • The deferred expressions are not read every time, they are requested
  • They do not impact performance during tracking
• What does the ‘X’ stand for?
  • Nothing!
• PyOrbit - what are the prospects of using it? Is it being phased out?
  • Trying to phase applications out of pyOrbit
  • Investment has been made into Xtrack
• Can the variations of components be saved in Xdeps
  • The idea is that things can be changed with complex rules while the simulation is running
• Xtrack does not use the same API as MAD-X, but in principle they are equivalent in capabilities
• How are you handling support?
  • Documentation
  • GitHub issues, classified as blocking or feature requests
  • Might be nice to have a tutorial for the next workshop?