1. Machine Learning based Data Analysis
Accelerator Directorate
Brendan O’Shea

2. Topics Ghost Imaging Neural Networks Reduce data requirements
Add measurement capabilities
Neural Networks
Speed up analysis
Reduce real time computational requirements

3. Data Analysis: Ghost Imaging
Ghost Imaging / Single Pixel Camera
Philosophy: don’t average out fluctuations… use fluctuations to probe your sample!
 “Compressive ghost imaging” leverages the ML toolbox. = B A x
Images, A
target reconstruction
B = [ 5037,
4783,
4891, … ]
Bucket measures total transmission
target
Random patterns
Siqi Li

4. Data Analysis: Ghost Imaging
Ghost imaging with electrons
photocathode quantum efficiency
UV laser
DMD
Two Differences:
Now cathode itself is the target
No DMD, instead exploit the natural cathode laser variation
bucket detector
Cathode laser
Cathode
target
# of e-
gun, solenoid
target
Reconstructed QE:
Siqi Li

5. Data Analysis: Ghost Imaging
Ghost imaging in the time domain
Pump X-ray
target
A. Picon
Delay (fsec)
Electron energy (eV)
Target (x)
Array of buckets (B)
Sample, CO
ProbeX-ray e-
Time
As system (CO) evolves after absorbing X-ray, electron energies change
Incident radiation (A)
Daniel Ratner

6. Topics Ghost Imaging Neural Networks Reduce data requirements
Add measurement capabilities
Neural Networks
Speed up analysis
Reduce real time computational requirements

7. Data Analysis: Computer vision for XFEL power
Power Prediction from with Vision-based Neural Network
XTCAV provides best record of X-ray power profile
Before lasing
Problem: Can only measure one image at a time
e- energy (g)
Alternative: Use computer vision on only lasing-ON image!
After lasing
e- energy (g)
Power (W)
Time (fs)
Xinyu Ren

8. Data Analysis: Computer vision for XFEL power
Power Prediction from with Vision-based Neural Network
XTCAV provides best record of X-ray power profile
Problem: Can only measure one image at a time
Alternative: Use computer vision on only lasing-ON image!
Unlabeled data
Labeled Training Set
Convolutional neural network (CNN) recovers X-ray power with smaller errors than current state-of-art
Training Set
Genesis Simulation
CNN
Experiment
GAN
Feed Training Data
Data Augmentation
The generative adversarial network (GAN) generates labeled images
 Augment training for CNN or other data-hungry algorithms
Xinyu Ren

9. Data Analysis: Temporal electric field reconstruction
Input power
Power reconstruction
Input spectral power
Maxwell, Timothy J., et al. International Society for Optics and Photonics, 2014.
Phase
reconstruction
a) Goal: full reconstruction of a femtosecond X-ray pulse (both power and phase)
b) Problem: temporal power diagnostics have bad resolution. No way to measure precisely.  No way to retrieve phase
c) Proposed solution: combine temporal+spectral diagnostics with iterative optimization... or with a neural network!
Xiao Zhang

10. Data Analysis: Prediction on simulated SASE dataset
Datasets:
Comparison with traditional iterative reconstruction:
Lasing off beam data
Simulated power profiles
Genesis
Power:
Phase: NN
0.08%
Iterative
0.22%
Power error
NN score: 0.83
Iterative score: 0.80
Phase score
Data Preprocessing:
normalization of input temporal power, spectral input power and the corresponding groundtruth. Then add smearing and noise
Networks:
Metrics:
Power
Phase
NN reconstruction: ~0.2ms per prediction
Iterative reconstruction: ~30s per prediction
Power
Phase
Xiao Zhang
 NN: ~ 105 faster

11. Edge Radiation Diagnostic
Filter
Quantify emittance and energy spread in a non-destructive manner
Edge Radiation based diagnostics naturally integrates into present linac designs
Diagnostic segments accelerator for better global tuning
Integrates into Gaussian Process, optimizer and other methods
Boost diagnostic speed and capabilities using Neural Network
A single shot, non-intercepting diagnostic ideal for efficient computer control of accelerators
Brendan O’Shea