1. Inter-experimental LHC Machine Learning Working Group Activities
Sergei V. Gleyzer
University of Florida

2. Big Data in Physics and Astronomy Sergei V. Gleyzer
Outline
Inter-experimental LHC Machine Learning Working Group
Machine Learning Applications at the LHC
Community Efforts
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

3. Big Data in Physics and Astronomy Sergei V. Gleyzer
IML
Inter-experimental LHC Machine Learning Working Group iml.cern.ch
Sharing of expertise among LHC (and other HEP) experiments
ATLAS, CMS, LHCb, ALICE, Belle-II, neutrino experiments
~450 participants
Exchange between particle physics and machine learning communities
Software development and maintenance
Forum, Training and Education
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

4. Big Data in Physics and Astronomy Sergei V. Gleyzer
Working Format
Monthly meetings around machine learning topics relevant to HEP community:
Deep Learning
Software and Tools
Hardware Applications
Unsupervised Learning
Anomaly Detection
Multi-class/Multi-objective Learning
Bayesian ML and GANs
Theory Applications
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

5. Big Data in Physics and Astronomy Sergei V. Gleyzer
IML Workshop
First IML Workshop at CERN
March 20-22, 2017
~300 participants
Industry session
Quark/gluon tagging challenge
Physics Object Tagging Workshop
HEP-ML Community White Paper
More workshops forthcoming
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

6. LHC Applications and Challenges
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Big Data in Physics and Astronomy Sergei V. Gleyzer

7. Big Data in Physics and Astronomy Sergei V. Gleyzer
LHC Applications
Primarily Classification
Low Level:
Particle identification
Photon or a jet?
Pattern recognition Tracks, vertices
High Level:
New Physics searches
Higgs/SUSY event or background?
Jet sub-structure
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

8. Big Data in Physics and Astronomy Sergei V. Gleyzer
Higgs Discovery
Machine Learning used in Higgs Discovery
Event selection
Identification of particles
Identification of interactions
Energy regression
Improvement in analysis
from all four areas
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

9. Big Data in Physics and Astronomy Sergei V. Gleyzer
Upcoming Challenges
Orders of magnitude between signals and backgrounds
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Big Data in Physics and Astronomy Sergei V. Gleyzer

10. Big Data in Physics and Astronomy Sergei V. Gleyzer
Event Complexity
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Big Data in Physics and Astronomy Sergei V. Gleyzer

11. Big Data in Physics and Astronomy Sergei V. Gleyzer
Upcoming Challenges
Data size:
LHC 15,000,000 Тb – 2035
Resources not up as fast as data volume
Unknown Physics
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

12. Big Data in Physics and Astronomy Sergei V. Gleyzer
Topics of interest
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

13. Object Identification
Interesting areas
Fast Simulation
Particle Tracking
Object Identification
Tracking: 100s particle trajectories low-level data Fast simulation: lots of computing power use, 10min/event Object id: Deep learning, use timing evolution
Trigger: Throwing away 99.9%, fast, smarter Calo: posed as an image problem More: correlations
Trigger
Imaging Calorimetry
Simulation
06/19/2017

14. Interesting areas Generative Models, Adversarial Networks Deep Kalman
RNNs
FCN, Recurrent, LSTM NN
Tracking: 100s particle trajectories low-level data Fast simulation: lots of computing power use, 10min/event Object id: Deep learning, use timing evolution
Trigger: Throwing away 99.9%, fast, smarter Calo: posed as an image problem More: correlations
Deep ML +FPGA
Convolutional DNN
Multiobjective Regression
06/19/2017

15. Big Data in Physics and Astronomy Sergei V. Gleyzer
Deep Learning
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

16. Big Data in Physics and Astronomy Sergei V. Gleyzer
Deep Learning
Higgs Boson Example:
P. Baldi, et. al. 2014
8% improvement
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Big Data in Physics and Astronomy Sergei V. Gleyzer

17. Big Data in Physics and Astronomy Sergei V. Gleyzer
Deep Learning
Papers in HEP:
Jet images and deep learning: arxiv
Jet substructure and deep learning: 
Parton shower uncertainties and jet substructure: 
Deep learning for ttHhttp://inspirehep.net/record/ ?ln=en
Nova 
Daya Bay arxiv
Next: 
Microboone:
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Big Data in Physics and Astronomy Sergei V. Gleyzer

18. Big Data in Physics and Astronomy Sergei V. Gleyzer
Deep Learning
Deep NN
Deep NN
BDT
Shallow
Significant improvements in performance
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Big Data in Physics and Astronomy Sergei V. Gleyzer

19. Big Data in Physics and Astronomy Sergei V. Gleyzer
Deep Learning
Convolutional Neural Networks
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

20. Big Data in Physics and Astronomy Sergei V. Gleyzer
End-to-End Approach
link
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Big Data in Physics and Astronomy Sergei V. Gleyzer

21. Big Data in Physics and Astronomy Sergei V. Gleyzer
CVN on NOvA
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

22. Beyond Classification
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

23. Single-Objective Regression
Train learning model to estimate a single function target or “objective”
Ex. photon energy/muon momentum
With a machine learning algorithm
Decision tree, random forest, neural network etc.
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

24. Big Data in Physics and Astronomy Sergei V. Gleyzer
Photon Energy
Single Target Example:
Inputs: shower information, photon coordinates, median event energy
Target Output: EMEASURED/ETRUE
~10-30% improvement in resolution
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

25. Deep Learning Regression
Deeper
Higher is better
Shallow
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

26. Multi-Objective Regression
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

27. Multi-Objective Regression
Simultaneous estimate of multiple functions or “targets”
Possibly additionally correlated
N single-target models not as optimal lingo: “multi-task” learning
and more cumbersome
Train a single model to simultaneously predict all targets
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

28. Big Data in Physics and Astronomy Sergei V. Gleyzer
Applicable Models
Methods:
Regression decision trees
Decision rules
Decision rule ensembles
Random forest
Neural networks…
Trade-offs:
accuracy, model size, interpretability
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

29. Multi-objective Example
X input variables {a, b, c, d…}
K of them strongly correlated
Y target outputs to estimate {A, B, C, D…}
N of them strongly correlated
Challenge: build a predictive model to describe simultaneously all the outputs {A,B,C,D…}, provided a corresponding set of inputs.
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

30. Big Data in Physics and Astronomy Sergei V. Gleyzer
Illustrative Example
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Big Data in Physics and Astronomy Sergei V. Gleyzer

31. Big Data in Physics and Astronomy Sergei V. Gleyzer
Target Correlations
Target Correlations
Prediction-Target Difference
Very close to Zero
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Big Data in Physics and Astronomy Sergei V. Gleyzer

32. Physics Object Tagging
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

33. Big Data in Physics and Astronomy Sergei V. Gleyzer
Object Tagging
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

34. Object Tagging
link
06/19/2017

35. Big Data in Physics and Astronomy Sergei V. Gleyzer
Tracking
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Big Data in Physics and Astronomy Sergei V. Gleyzer

36. HEP.TrackX
link
06/19/2017

37. HEP.TrackX
link
06/19/2017

38. Big Data in Physics and Astronomy Sergei V. Gleyzer
Software and Tools
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Big Data in Physics and Astronomy Sergei V. Gleyzer

39. Big Data in Physics and Astronomy Sergei V. Gleyzer
DNN Apache Spark
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

40. Big Data in Physics and Astronomy Sergei V. Gleyzer
Deep Learning
Throughput Comparison
2.7 * Theano
Single precision
Excellent throughput compared to Theano
on same GPU
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

41. Hardware Applications
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

42. Big Data in Physics and Astronomy Sergei V. Gleyzer
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

43. Big Data in Physics and Astronomy Sergei V. Gleyzer
Neuromorphic
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

44. Theory and Phenomenology
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

45. Big Data in Physics and Astronomy Sergei V. Gleyzer
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

46. Big Data in Physics and Astronomy Sergei V. Gleyzer
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

47. Big Data in Physics and Astronomy Sergei V. Gleyzer
link
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

48. Big Data in Physics and Astronomy Sergei V. Gleyzer
Other areas
Unsupervised Learning and Anomaly Detection
Generative adversarial models for fast detector simulation
Multi-class applications
Understanding uncertainties associated with decision-making in machine learning applications
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

49. HEP Community White Paper in Machine Learning
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

50. Big Data in Physics and Astronomy Sergei V. Gleyzer
Community White Paper
HEP Software Foundation
HSF link
Community White Paper
link to CWP
Machine Learning
Identification of challenges
Roadmap to address them
Important to think of these issues now
Impact on how we dedicate resources and design our software
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer

51. Big Data in Physics and Astronomy Sergei V. Gleyzer
Summary
LHC physics and computing challenges will require significant progress:
Higher backgrounds and pileup, data volume, unknown new physics
Machine learning offers a promising direction
An opportunity to examine new areas of ML applications to HEP
IML an inter-experimental effort to foster collaboration and progress in HEP-ML
06/19/2017
Big Data in Physics and Astronomy Sergei V. Gleyzer