1. Machine Learning Study
SPEAR3 BTS Injection Efficiency
Faya Wang and Xiaobiao Huang
Feb

2. Outline
Introduction of BTS injection efficiency
Raw data study
Neural network for machine learning
Learning from the model

3. BOOST to SPEAR3 (BTS) layout
Injection efficiency from BOOST to SPEAR3 is affected by
Injected beam emittance and trajectory
SPEAR3 dynamic aperture
Environment (like temperature)
SPEAR3
BOOST

4. Injection efficiency data
Injection efficiency measured by 3 different methods: ACM, ToroidBeam, BOO-QM
2017
2018
2019
BOO-QM: the most high quality data (less noise and more accuracy)

5. Injection efficiency data preparation – Clean up
Cut injeff above 200% and below 50%
Remove jitter
Total of 2.96% of data are manipulated by the process.
2017
2018
2019

6. Beam trajectory in BTS [G=FY17, B=FY18, Y=FY19]

7. Overall beam trajectory -- upstream

8. Overall beam trajectory -- downstream

9. SPEAR3 setup: Undulator gap

10. Enviorment: Temperature
Outside

11. Correlation of beam trajectory and injection efficiency
FY17
FY18
FY19
injection efficiency by 3 different methods

12. Correlation of temperatures and injection efficiency
FY17
FY18
FY19
Ambient temperature
Ground temperature

13. Correlation of gaps and injection efficiency
FY17
FY18
FY19

14. 2 Model: inputs
Injection efficiency is a affected by beam trajectory, SPEAR3 acceptance and some hidden features.
As some downstream steering will affect beam which will not be able to captured by BPMs, upstream BPMs together with downstream steering will be used as the inputs
Inputs are:
upstream BPMS (10)
downstream steerings (7)
Temperature (2)
undulator gaps (3)
Total of 22 variables.

15. 3. Data setup Total number of data entries: 130698
40 days of data are used for test
Training
Validation
Test
63.8%
27.4
8.8
Each block = 2 days

16. 5 layers of network (include Dropout to reduce overfitting)
4. Neural network setup
5 layers of network (include Dropout to reduce overfitting)
1st layer: RNN(LSTM), hidden layers 30, drop rate 0.5
2nd layer: CNN, hidden layers 30, drop rate 0.5
3rd layer: CNN, hidden layers 20, drop rate 0.25
4th layer: CNN, hidden layers 10, drop rate 0
5th layer: output
Total params: 5611
Validation STD = 3.36

17. 5. Test results with 40 days
Test STD = 4.42

18. Study of temperature and gap effects with the model

19. Prediction for all the data with the model

20. Temperature and “Gap05” affects

21. Temperature and Gap05 affects
Major contribution

22. Ideal beam orbit at different temperatures
Feed all the beam trajectory (BPM readings) and steering current to the model
Take beam trajectory and steering current within the top 10% of the injection efficiency
Major contribution from ground temperature

23. Ideal beam orbit at different temperatures - BPMs
[24 degC]
[30 degC]
Ideal orbit by the BPM readings at the density peak

24. Ideal beam orbit at different temperatures - BPMs

25. Summary
A shallow neural network model has been built which is able to obtain reasonable results
With the model, we learned
The major hidden figure that affects BTS injection efficiency is the ground temperature
How the idea beam orbit manipulated by the ground temperature