Fast particle accelerator optics simulation for reinforcement learning and optimisation applications.
Project description
Cheetah
Cheetah is a particle tracking accelerator we built specifically to speed up the training of reinforcement learning models.
Installation
Simply install Cheetah from PyPI by running the following command.
pip install cheetah-accelerator
How To Use
A sequence of accelerator elements (or a lattice) is called a Segment
in Cheetah. You can create a Segment
as follows
segment = Segment([
BPM(name="BPM1SMATCH"),
Drift(length=1.0),
BPM(name="BPM6SMATCH"),
Drift(length=1.0),
VerticalCorrector(length=0.3, name="V7SMATCH"),
Drift(length=0.2),
HorizontalCorrector(length=0.3, name="H10SMATCH"),
Drift(length=7.0),
HorizontalCorrector(length=0.3, name="H12SMATCH"),
Drift(length=0.05),
BPM(name="BPM13SMATCH"),
])
Alternatively you can create a segment from an Ocelot cell by running
segment = Segment.from_ocelot(cell)
All elements can be accesses as a property of the segment via their name. The strength of a quadrupole named AREAMQZM2 for example, may be set by running
segment.AREAMQZM2.k1 = 4.2
In order to track a beam through the segment, simply call the segment like so
outgoing_beam = segment(incoming_beam)
You can choose to track either a beam defined by its parameters (fast) or by its particles (precise). Cheetah defines two different beam classes for this purpose and beams may be created by
beam1 = ParameterBeam.from_parameters()
beam2 = ParticleBeam.from_parameters()
It is also possible to load beams from Ocelot ParticleArray
or Astra particle distribution files for both types of beam
ocelot_beam = ParticleBeam.from_ocelot(parray)
astra_beam = ParticleBeam.from_astra(filepath)
You may plot a segment with reference particle traces bay calling
segment.plot_overview(beam=beam)
where the optional keyword argument beam
is the incoming beam represented by the reference particles. Cheetah will use a default incoming beam, if no beam is passed.
Cite Cheetah
To cite Cheetah in publications:
@inproceedings{stein2022accelerating,
author = {Stein, Oliver and
Kaiser, Jan and
Eichler, Annika},
title = {Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications},
booktitle = {Proceedings of the 13th International Particle Accelerator Conference},
year = {2022}
}
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