Machine Learning Study

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Presentation transcript:

Machine Learning Study SPEAR3 BTS Injection Efficiency Faya Wang and Xiaobiao Huang Feb. 19 2019

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

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

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)

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

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

Overall beam trajectory -- upstream

Overall beam trajectory -- downstream

SPEAR3 setup: Undulator gap

Enviorment: Temperature Outside

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

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

Correlation of gaps and injection efficiency FY17 FY18 FY19

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.

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

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

5. Test results with 40 days Test STD = 4.42

Study of temperature and gap effects with the model

Prediction for all the data with the model

Temperature and “Gap05” affects

Temperature and Gap05 affects Major contribution

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

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

Ideal beam orbit at different temperatures - BPMs

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