A New Approach For Canadian Light Source Future Orbit Correction System Driven
By Neural Network
Shervin Saadat 1,* and M. J. Boland 1,2Canadian Light Source, S7N 2V3 Saskatoon, Canada
2 University of Saskatchewan, Department of Physics and Engineering Physics, S7N 5E2 Saskatoon, Canada
1* shervin.saadat@lightsource.ca

Abstract

The Canadian Light Source (CLS) uses a 48-set orbit correction system (OCS) with BERGOZ beam position monitors (BPM) to track beam positions at 900 Hz. The Inverse
Response Matrix (IRM) is employed to optimize beam path using 48 sets of orbit correctors. The study proposes replacing SVD with the neural network algorithm, featuring a3-layer model with 96 nodes each. The neural network's typical MSE in CLS operations is 10-7. Testing the OCS at 8.0 mA with various challenges showed the new model
effectively generated required orbit corrector signals.

Canadian Light Source

CLS (Canadian Light Source) is a synchrotron light source. There are 12
sections in the storage ring, which runs at 2.9 GeV. Every cell contains four
horizontal and vertical beam position monitors (BPMs), and 4 orbit correctormagnets (OCMs).
The SVD can be calculated at all BPMs, by adjusting the strength of eachOCM.

Neural Network Model

The first layer should consist of 96 neurons for 96 BPMs data, because thenumber of neurons depends on the inputs, in addition, there are 96 outputs
for 96 OCMs.
The same number of inputs and outputs prevent us from expanding nodes inhidden layers, so we select 96 nodes per hidden layer. Modeling of theTensorFlow-Keras library was performed, MSE was around 10−7.
The Hyperbolic Tangent (𝑇𝑎𝑛ℎ) was used because the input data changed
between -1 and 1. 70 percent of the database was used for training and 30percent for testing and finally the model was created.
The test mean square error is (3.1524 ∗ 10−7) and the train mean square erroris (3.1522 ∗ 10−7) with 20 epochs. The MSE values of testing and training
indicate that the new model is fairly accurate.
The initial model demonstrated real-time accuracy on stable data withminimal errors. To accommodate significant beam position changes, theneural network must be versatile, requiring diverse training data. A learning
loop is implemented, evaluating new datasets with the initial model,
updating it as needed for improved accuracy based on error thresholds,ensuring the best-performing model replaces the previous one.

In this experiment, the BPM's values in different sections of the SR werechanged by adjusting the reference (offset) to create a desired beam position
situation(+1 and -1 mm) for the OC system. The system was tested at a beam
current of 8.0 mA.

Conclusion

Experiments and Results

In the first scenario, when the system is functioning normally, we conducted thelearning loop four times. During the first phase, we developed the model, while
the second to fourth phases involved updating the model.
In the second scenario, the beam position is expected to undergo a suddenchange, presenting a challenging situation for the OCS. We aim to investigate
whether this loop can update a model with memorized coefficients,
furthermore, we will calculate the error that arises during this process*.
* We introduced a metric called Accumulated Error (AE), which represents the sum of errors across all OCs.

Two conditions of the NN model's effectiveness were investigated, a stablemode and a complex mode. The results for the stable mode demonstrate thaterror rates decrease over time, from 1.1% per OC in the first experiment to0.42% per OC after four steps. While this result was expected, it provides
further confirmation of the effective operation of the deep learning loops in theNN model, which successfully reduces error rates over time in the stable modecondition. The results of the second scenario are less clear than those of thestable mode upgrade, although they do exhibit a decreasing trend in error rates
despite some fluctuation(Last Picture). There shows a decreasing trend in
prediction errors for a specific data set(upgrades1 and 2), but the deep learningmodel is unable to generalize its performance to new data sets(upgrade 3)
without further training. However, next step training result in reduced errors on
new data sets(upgrades 4, 5). This cycle continues, and the errors decreasesmoothly over time.

The authors would like to express their gratitude to Tonia Batten, Johannes Vogt, Ward Wurtz,Cameron Baribeau, Hamed Shaker, Drew Bertwistle, Mike Bree, Fredric Le Pimpec, and allother AOD members of the CLS operation group for their contributions and support.