Using Machine Learning for IPM profile reconstruction
D. Vilsmeier, M. Sapinski and R. Singh
(d.vilsmeier@gsi.de)
Can we use Machine Learning for IPM profile reconstruction?
 What is Machine Learning?
 What is spacecharge induced profile distortion?
 How can we simulate this process?
 What machine learning techniques can be applied?
 How can we verify the simulation?
 What's next?
What is Machine Learning?
Field of study that gives computers the ability to learn without being explicitly programmed.
 Arthur Samuel (1959)
"Classical"
approach:
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Input
Algorithm
Output
Machine
Learning:
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Input
Algorithm
Output
What is Machine Learning really?
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
 Tom Mitchell (1997)
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=
Input
Algorithm
Output
Input
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Output
Experience
Performance
Task
A Brief History of AI
Arthur Samuel
Checkers
1959
Minsky, Papert
Perceptron limits
1969
Hans Moravec
Autonomously driving car
1979
1992
TDGammon
Gerald Tesauro
1997
Deep Blue
IBM
2015
Playing Atari Games
DeepMind
2017
AlphaZero
DeepMind
Machine Learning Toolbox
Supervised Learning:
 Artificial Neural Networks
 Decision Trees
 Linear Regression
 kNearest Neighbor
 Support Vector Machines
 Random Forest
 ... and many more
Unsupervised Learning:
 kMeans Clustering
 Autoencoders
 Principal comp. analysis
Reinforcement Learning:
 QLearning
 Deep Deterministic Policy Gradient
Ionization Profile Monitors
 Measure the transverse profile of particle beams
 Beam ionizes the rest gas
ðŸ¡’ measure the ionization products
 Electric guiding field is used to transport ions / electrons to the detector
 For electrons optionally apply a magnetic field to confine trajectories
The Problem
Extracting the ions / electrons from the ionization region is expected to provide a onedimensional projection of the transverse beam profile, but ...
Protons  
Energy  6.5 TeV 
ppb  2.1e11 
Ïƒ_x  0.27 mm 
Ïƒ_y  0.36 mm 
4Ïƒ_z  0.9 ns 
What happens?
Ideal case:
Particles move on straight lines towards the detector
Real case:
Trajectories are distorted due to various effects
IPM Profile Distortion
Initial momenta:
Â

Initial velocity obtained in the ionization process may have a component along the profile
Â  Becomes significant for small extraction fields / small beam sizes
Interaction with beam fields:
Â

Close to the beam particles interact with the beam fields, resulting in distorted trajectories
Â  Becomes significant for large beam fields, e.g. high charge densities or beam energies
+ instrumental effects such as camera tilt, pointspreadfunctions from optical system and multichannelplate granularity, etc.
Countermeasures
Increase electric guiding field
Increased electric field ðŸ¡’ smaller extraction times ðŸ¡’ smaller displacement
Additional magnetic guiding field
Increased magnetic field ðŸ¡’ smaller gyroradius ðŸ¡’ smaller possible displacement
Spacecharge interaction
6.5 TeV, 2.1e11 Protons, Ïƒ = (270, 360) Î¼m, 4Ïƒz = 0.9ns
 Interaction with beam fields can become very strong ðŸ¡’ extraction fields are not sufficient to suppress distortion (typical values are 50 kV/m)
 Spacecharge interaction results in a vast increase of gyrovelocities and hence in large displacements
Spacecharge interaction

Electron velocities oscillate in the electric field of the beam
Â  They level off at an increased velocity, hence increasing distortion
 The amplitude of this oscillation depends on the initial offset to the beam center
Analytical considerations
Actually this type of oscillation can be seen from analytical considerations, using a simplified, namely linear, model for the beam electric field. This assumption holds well for the region close to the beam center.
Â
The corresponding equations of motion are (for an electron):
Analytical considerations (II)
Defining
One arrives at the following expressions for the velocity:
What about particles that have their velocity decreased?
Initial parameters:
Compare with particle that has its velocity increased:
Gyromotion
Spacecharge region
Detector region
Electron motion is different for two distinct regions:
 Spacecharge region: beam field influences velocity
 Detector region: pure gyromotion without beam field influence
Distortion through gyromotion
Gyromotion is parametrized via
Particle spirals around
Â
and will be detected in the range
Detection probabilities
Provided that the moment of detection is random, the gyromotion of electrons gives rise to probabilities of being detected at a specific position.
Applying the limit results in divergence at the "edges" of the motion, so the expression is only valid for the center. For real profiles however we can work with a discretized version of the abovementioned relation.
small statistics
Summary of displacements
Â position at ionization
Â gyration center (detector region)
Â position of detection
Two types of displacement:
Â
 Â Â Â Â : Depends on initial zvelocity, transverse electric field, ExBdrift by long. electric field
Â  Â Â Â Â : Displacement due to gyration motion; stochastic displacement
Analytical description?
In principal zvelocities are relatively small (shift ~ 10 Î¼m; ionized electrons are mainly scattered in transverse direction), the longitudinal electric field can be neglected and the center shift due to the transverse electric field is around a few micrometers which is small compared to the magnitude of the gyromotion.
Describing the gyrodistortion via pointspreadfunctions is however not possible because the gyrovelocity increase is not uniform along the profile.
Simulating the process
VirtualIPM simulation tool was used to generate the data
Parameter  Range  Step size 

Bunch pop. [1e11]  1.1  2.1 ppb  0.1 ppb 
Bunch width (1Ïƒ)  270  370 Î¼m  5 Î¼m 
Bunch height (1Ïƒ)  360  600 Î¼m  20 Î¼m 
Bunch length (4Ïƒ)  0.9  1.2 ns  0.05 ns 
Protons
6.5 TeV
4kV / 85mm
0.2 T
Â =Â 21,021 casesÂ Â 5h / caseÂ Â 12 yrsÂ  Â Â Good thing there are computing clusters :)
Physics models
Analytical expression for the electric field of a twodimensional Gaussian charge distribution
ðŸ¡’ Longitudinal field is neglected; justifiable for highly relativistic beam
ðŸ¡’ Magnetic field is considered
M. Bassetti and G.A. Erskine, "Closed expression for the electrical field of a twodimensional Gaussian charge", CERNISRTH/8006, 1980
Double differential cross section for relativistic incident particles
A. Voitkiv et al, "Hydrogen and helium ionization by relativistic projectiles in collisions with small momentum transfer", J.Phys.B: At.Mol.Opt.Phys, vol.32, 1999
Uniform electric and magnetic guiding fields
Simulated profiles
Simulation outputs particle parameters as csv data frame
Summarize initial and final positions intoÂ 1 Î¼m histograms ranging [10, 10] mm
Rebin to 55 Î¼m (silicon pixel detector) and 100 Î¼m (optical acquisition system)
Bunch population and bunch length
&
Data preparation
ppb  4Ïƒz  f [1]  ...  f [n]  i [1]  ...  i [n] 
Data
Measured profile
Beam profile
Input
Output
Drop zero variance variables (as they don't provide any information)
Rescale data to have zero mean and unit variance: Â Â Â Â Â Â Â Â Â Â Â Â Â
Compute Ïƒ from profile
Optionally rescale
Train, Test, Validate
Divide the machine "learning" into three stages and corresponding data sets:
Training
Validation
Testing
This data set is used to derive rules from the data, i.e. to fit the model (e.g. polynomial fit). Split size ~ 60%.
This data set is used to ensure that the model generalizes well to unseen data, between multiple iterations of hyperparameter tuning (e.g. polynomial degree) and model selection. Split size ~ 20%.
This data set is used for evaluating the performance of the final model (in order to prevent "tuningbias"). Split size ~ 20%.
Evaluating performance
Explained variance
Mean squared error
R2score
Anscombe's quartet
Â
Similar mean, variance and R2 (approx.)
Â
Residuals plot is important
Â
Visualization helps in assessing the quality of a model
Multivariable linear regression
Requirements:
 Predictors are errorfree
 Absence of multicollinearity among the predictors
predictors
intercept
error term
Multivariable linear regression  Results
Explained variance  0.99972 
R2score  0.99972 
Mean squared error  0.25212 
Kernel ridge regression
Twofold extension of "classical" linear regression:
 Uses ridge regularization for the learned weights (L2norm)
 Employs the "Kernel trick"
L2 "ridge" regularization:
Adding the norm of the weights to the loss function puts a penalty on the weights.
Strength of penalization can be adjusted.
Regularization term prevents overfitting
The Kernel Trick
Features can't be separated by a linear function ...
... at least not in twodimensional space.
Define a mapping, for example:
Now the space in which K maps may be very high dimensional, so computing the corresponding vectors is expensive.
The trick is to express the dot product of K(x, y) in terms of the initial vectors (x, y) ðŸ¡’ no need to compute K(x, y).
Kernel ridge regression  Results
Polynomial kernel
Radial basis function kernel
degree = 2
Poly kernel  RBF kernel  
Explained variance  0.99983  0.99974 
R2score  0.99983  0.99974 
Mean squared error  0.15074  0.23331 
Support Vector Machine
 Originally developed for classification tasks
 Find a linear separation of two classes with maximum margin between them
prediction
learned weights
class labels
training data
intercept of hyperplane
Fitting and predicting only depends on dot products of feature vectors, so we can use the kernel trick
Support Vector Regression
SVM
hinge loss
SVR
Îµinsensitive loss
The fitted model is similar to Kernel Ridge Regression but the loss function is different (KRR uses ordinary least squares)
Support Vector Regression  Results
Explained variance  0.99985 
R2score  0.99985 
Mean squared error  0.13527 
Artificial Neural Networks
Inspired by the human brain, many "neurons" linked together
Input layer
Weights
Bias
Apply nonlinearity, e.g. sigmoid, tanh
Perceptron
MultiLayer Perceptron
Deep Learning
What means deep?
Usually two or more hidden layers.
Using fully connected layers, the number of parameters can grow quite large (e.g. one of the MNIST top classifiers uses 6 layers = 11,972,510 parameters).
All those parameters are adjusted using the training data.
How deep networks learn
Define a loss function that assesses how good the network's predictions are, e.g. mean squared error:
Compute the gradient of the loss with respect to network parameters, e.g. Gradient Descent or Adam (uses "momentum").
Update the network's parameters by applying the gradient with a learning rate:
Deep Learning Frameworks
ANN Architecture
IDense = partial(Dense, kernel_initializer=VarianceScaling())
# Create feedforward network.
model = Sequential()
# Since this is the first hidden layer we also need to specify
# the shape of the input data (49 predictors).
model.add(IDense(200, activation='relu', input_shape=(49,))
model.add(IDense(170, activation='relu'))
model.add(IDense(140, activation='relu'))
model.add(IDense(110, activation='relu'))
# The network's output (beam sigma). This uses linear activation.
model.add(IDense(1))
Activation function
Fullyconnected (dense) feedforward (sequential) architecture
ANN Learning
model.compile(
optimizer=Adam(lr=0.001),
loss='mean_squared_error'
)
model.fit(
x_train, y_train,
batch_size=8, epochs=100, shuffle=True,
validation_data=(x_val, y_val)
)
D. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization", arXiv:1412.6980, 2014
Other possible optimizers are Stochastic Gradient Descent for example.
Batch learning
 Training is an iterative procedure
 Need to go through the data set multiple times (= 1 epoch)
 Weight updates are performed in batches (ðŸ¡’ batch size)
After each epoch compute the loss on the validation data in order to prevent overfitting
Could apply early stopping
ANN  Results
Explained variance  0.99988 
R2score  0.99988 
Mean squared error  0.10959 
Summary of model performance
Î¼ (res.)  Ïƒ (res.)  R2score  MSE  

Linear  0.000213  0.502  0.99972  0.22512 
KRR  0.00590  0.388  0.99983  0.15074 
SVR  0.00111  0.368  0.99985  0.13527 
ANN  0.00725  0.331  0.99988  0.10959 
Values are given in units of
 Artificial neural network (multilayer perceptron) showed the best performance
 Although the residuals changed with sigma, no bias occurred and beam sigma is expected to be in the center of the range anyway
Performance on test data
Explained variance  0.99988 
R2score  0.99988 
Mean squared error  0.10815 
Using ANN
Measuring spacecharge induced profile distortion
Measurements taken at CERN / SPS / BGIH
Electric field  4 kV / 85 mm 
Magnetic field  0.2 T (50A) 
At the max. magnetic field no profile distortion occurs, but we can reduce the magnet current to make that happen
Acquisition system:
MCP + Phosphor + Camera
Protons  

Energy  400 GeV 
Bunch pop.  2.859e11 
Beam width (1Ïƒ)  0.835 mm 
Beam height (1Ïƒ)  0.451 mm 
Bunch length (4Ïƒ)  1.6 ns 
The Measurements
Taking into account:
 Pixeltomm calibration
 Camera rotation
 Optical PSF (~ 130 Î¼m)
Wire Scanners were used to obtain undistorted reference profiles, however only the sigma values from fit were available
Bad luck with the RNG
4 A
16 mT
20 A
80 mT
What about the PSF?
Trying a 520 Î¼m pointspread function instead:
This resolves the deviations for all magnetic field values.
4 A
16 mT
20 A
80 mT
However such a large PSF is difficult to explain.
Next steps
Hyperparameter tuning is tedious but there are helpful tools
Collect more data from measurements
Test the method against measured reference profiles (e.g. from Wire Scanners)
Test the method with "unclean" data (e.g. noise added, PSF applied)
from sklearn.model_selection \
import GridSearchCV
from autosklearn.regression \
import AutoSklearnRegressor
Investigate the effect of different profile binnings, i.e. different number of predictors
All models are wrong but some are useful.
 George Box (1978)
Piqued your interest?
Icons by icons8.
Please find more information at https://ipmsim.gitlab.io/IPMSim/
The data presented in this talk was generated with VirtualIPM
Using Machine Learning for IPM profile reconstruction
By Dominik Vilsmeier
Using Machine Learning for IPM profile reconstruction
Operations Beam Physics and Techniques Salon, GSI Helmholtz Centre for Heavy Ion Research (Darmstadt, Germany), February 20th 2018
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