ir_amplitude_detuning 1.0.0

Python tools to correct amplitude detuning in the insertion regions of a particle accelerator.

Corrections from Amplitude Detuning

This package provides tools to examine measured amplitude detuning and calculate corrections based on the measurements. There is also a lot of handy functionality to assess the quality of correction and compare different correction schemes.

The heart of the package is the creation of a linear equation system (optionally with constraints), which reflects the contribution of the different corrector magnets to amplitude terms, including feed-down effects, on the left hand side, and the measured amplitude detuning values on the right hand side. This equation system is then solved via least-squares minimization to find the optimal corrector settings to correct the measured amplitude detuning.

To create this equation system, the user can define different Targets. Each Target has a name and a list of TargetData instances, in which the user can define individual detuning contributions to be corrected, e.g. using different correctors, optics, measured values and constraints. All target data within one target are matched together, i.e. transformed into a single equation system. Targets themselves are treated independently, so that the user can compare which correction approach worked best.

A detailed description of the underlying theory can be found in Dilly - Thesis 2024.

Installation

The package can be installed via pip:

pip install ir-amplitude-detuning

Examples

Several examples for different LHC years and measurement setups can be found in the examples/ folder. These will not be installed with the package, so please clone the repository if you want to use them.

The examples used here, are particularly tailored to correct the detuning changes with and without the crossing scheme in the LHC IRs, i.e. to compensate the detuning from feed-down effects. They only use normal-oriented dodecapole correctors. In principle, the package can be used to correct also other detuning contributions, and use e.g. octuople or decapole correctors. Only normal-oriented correctors are implemented so far.

The examples focus on the correction of first-order amplitude detuning terms (dQ/d2J), but correction of second-order terms is also implemented.

Setup Template

A template for the setup can be found in setup_template.py, which can be used as a starting point for your own setups. As it is only a template, it might be easier to look at the examples for actual setups to understand how to use the package.

The important part for correction is the defintion of the Targets and the call to the calculate_corrections function, yet usually the optics for the machine need to be simulated first.

The worklow is usually as follows:

1. Simulate the machine and get the optics for the given setups (in exampels: different crossing schemes). The setup parameters are defined in the SimParams container.
   This step is optional if you already have the optics (twiss) as DataFrames at hand.

[!WARNING] If you are providing MAD-X or MAD-NG twiss output, always use beam 4.

2. Plug in the measured amplitude detuning values into the Measurements container.
   These could be directly put into the targets, but usually it's easier to define them separately.

3. Define correction targets: apart from the name, these consist of the detuning values to be corrected. These are be grouped into different TargetData instances, which can e.g. contain different setups of the machine (crossing schemes, optics, etc.,) given as twiss DataFrames loaded from the simulation output.

4. Run the correction calculation with the defined targets and corrector field orders, this gives the corrector settings per Target to apply to the machine.

[!WARNING] This assumes the corrector circuits/variables are implemented with positive sign leading to positive gradients (KN, KNL) in the magnets as seen from beam 1 and with the respective anti-symmetry compensating signs (e.g. in normal-oriented quadrupoles) in beam 4.

5. Assess the correction quality by calculating the contributions analytically. This is fast and allows to compare different sources (e.g. different corrector field orders or different IPs) at a single Target in addition to comparing the different Targets with each other.

6. Assess the correction quality by re-simulating the machine with the applied corrector settings (implemented: Using Mad-X/PTC). This is slower, but gives a more realistic picture of the correction quality, as all non-linear effects and feed-downs are taken into account.

7. Plot the results to visualize the correction quality, e.g. by comparing the detuning before and after correction or how well the correction matches the target detuning.

A lot of details about the individual steps can be found in the examples.

2022 Commissioning Example

An example for the 2022 commissioning measurements can be found in examples/commissioning_2022.py. This example is the most basic one and implements only a single Target:

• Target 1: Correcting the detuning change between full crossing scheme enabled and flat orbit (a single TargetData).

2018 MD3311 Example

A slightly more complex example for the 2018 MD3311 measurements can be found in examples/md3311.py. Here, three different TArgets are defined:

• Target 1: Correcting the detuning change between full crossing scheme enabled and flat orbit.
• Target 2: As Target 1, but additionally targeting the detuning stemming from the crossing in IP5 only.
• Target 3: As Target 2, but with additional constraints on the cross-term to stay below zero.

2022 MD6863 Example

In the example examples/md6863.py, the measurements from MD6863 in 2022 are directly loaded from the analysed detuning output files from omc3, which makes already the first step more complex in code and requires a well defined naming scheme for the files.

Two different Targets are defined here:

• Target 1: Correcting the detuning change between full crossing scheme enabled and flat orbit.
• Target 2: As above, but targeting also the detuning stemming from IR5 only, with positive and negative crossing angles in IP5.

Target 2 allows to better differenciate between the decapole and dodecapole contributions,

Output

In the respective output directory and its sub-directories, the following files can be found.

The sub-directories are the output directories of the individual machine-setup simulations per beam. They contain the beam in their name (e.g. b1) and if multiple machine setups are required, they are additionally prefixed with label. In these sub-directories the following files can be found:

• ampdet.lhc.b#.nominal.tfs: Output of PTC containing the amplitude detuning data.
• twiss.lhc.b#.nominal.tfs: Output of the twiss command, containing the optics.
• twiss.lhc.b#.optics_ir.tfs: Output of the twiss command, containing only the IR optics (not used, only for user convenience).
• full_output.log: Logging output. In the log of the last run beam, also the logging from the intermediate python can be found.
• madx_commads.log: Mad-X commands used in this run.

In the main output directory the results of the actual correction calculation, their efficiency checks and plotting can be found. These usually rely on multiple of the optics, i.e. the output of the twiss command, from the simulations above, in particular for both beams. Files of the same type are identifyable by their output_id, which is either the name given to the Targets or nominal for the machine without any targets applied.

• ampdet.lhc.b#.output_id.tfs: Output of PTC containing amplitude detuning data (after applying the found corrections or nominal).
• ampdet_calc.lhc.b#.output_id.tfs: Output of containing amplitude detuning data calcualted from the found corrections.
• settings.lhc.b#.output_id.tfs: Table containing the corrector settings to match the amplitude detuning for the given target, as well as additional information if applicable, such as error-bar, KN and KNL value, length of the magnet, magnet name and circuit name.
• settings.lhc.b#.output_id.madx: A madx command, assigning the calculated corrector values to the circuits.
• twiss.lhc.b#.output_id.tfs: Output of the twiss command, containing the optics (after applying the found correction or nominal).
• plot.*.pdf: Different plots comparing the detuning before and after correction, as well as the calculated detuning from the applied corrector settings.

Different Machines and Codes

The package is designed to be extensible to different machines and simulation codes, yet so far only the LHC is implemented in Mad-X via cpymad. To add a different machine, you will need to implement a workflow that creates the optics as DataFrames. The easiest way is to stick to the same output format and naming conventions as are used in the LHC simulation part of this package. A big concession to the LHC layout is the assumption of IPs and beams, which might not be present in other machines. You should be able to work around this by simply assigning None to the ip attribute of correctors and using only beam 1 (beam 2 and beam 4 trigger some special behaviour in the calculations to take beam direction and mad-x conventions into account). Beware: This has not been tested yet!

Development

A uv lockfile is included in the repository to easily set up a development environment. One can get setup with uv by running in the root of this repository:

uv sync