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Machine Learning in New Physics Exploration

This article discusses the application of machine learning in exploring new physics, specifically in the context of SUSY and LHC. It covers topics such as neural networks, graph neural networks, event selection methods, and the pros and cons of different machine learning approaches.

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Machine Learning in New Physics Exploration

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  1. Machine Learning in New Physics Exploration Jin Min Yang ITP, Beijing

  2. Contents • Introduction • Machine learning scan in SUSY • Graph neural network for stop pair at LHC • Graph neural network for at LHC • Conclusion

  3. 1 Introduction

  4. Neural Network • Warren McCulloch and Walter Pitts (1943) created the first neural network based on mathematics and algorithms called threshold logic. • The perceptron algorithm was invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. • For multilayer perceptron (feed-forward neural network), where at least one hidden layer exists, more sophisticated algorithms such as backpropagation (Rumelhart, Hinton and Williams, 1986) must be used.

  5. Neural Network output input hidden input output

  6. Difficult to interpret • (crucial for physics but not for industry)

  7. Interpretiveness is crucial for physics ! The history of astronomy shows that observations can only explain so much without the interpretive frame of theories and models. Big data and machine learning are powering new approaches to many scientific questions. But the history of astronomy offers an interesting perspective on how data informs science—and perhaps a cautionary tale. The big lesson is that big data doesn't interpret itself. Making mathematical models, trying to keep them simple, connecting to the fullness of reality and aspiring to perfection—these are proven ways to refine the raw ore of data into precious jewels of meaning.

  8. AI is only statistics ??

  9. 10/62 Machine Learning in HEP GOAL • “Solve” HEP problems using DATA EXAMPLE • Physics model selection • Scan (e.g. 1011.4306, 1106.4613, 1703.01309, 1708.06615) • Collider • Parton distribution function (e.g. 1605.04345) • Object reconstruction (e.g. NIPS-DLPS) • Pileup mitigation (e.g. 1512.04672, 1707.08600) • Jet tagging (e.g. 1407.5675, 1501.05968, 1612.01551, 1702.00748) • Event selection (e.g. 1402.4735, 1708.07034, 1807.09088) • Decayed object reconstruction • Anomaly event detection (e.g. 1807.10261)

  10. 11/60

  11. arXiv:1905.06047

  12. 12/60 Our Work • Machine learning in SUSY parameter space exploration 1708.06615 J. Ren, L. Wu, JMY, J. Zhao • Graph neural network for stops at LHC 1807.09088 M Abdu, J Ren, L Wu, JMY • Graph neural network for search at LHC 1901.05627 J Ren, L Wu, JMY

  13. 10/60 2 Machine Learning Scan in SUSY 1708.06615 J Ren, L Wu, JMY, J Zhao

  14. 14/60

  15. 15/60 MSSM alignment limit

  16. 16/60

  17. 17/60 Gray points—wasted Blue points --survived

  18. Iteration 1 Back

  19. Iteration 2 Back

  20. Iteration 3 Back

  21. Iteration 4 Back

  22. Iteration 5 Back

  23. Iteration 10 Back

  24. Iteration 20 Back

  25. Iteration 50 Back

  26. Iteration 100 Back

  27. 27/60 CMSSM/mSUGRA

  28. 28/60

  29. 29/60 3 Graph Neural Network for stops at LHC 1807.09088 M Abdu, J Ren, L Wu, JMY An eventis a signalor background ?

  30. 13/60 Methods for Event Selection • Cut-flow B S Signal Region Control Region

  31. 31/60 Methods for Event Selection • Cut-flow • Machine Learning • Boosted Decision Tree (BDT) a lot of trees  a forest When an event comes, it passes each tree and is valued 1(signal) or 0(background). Finally, these values are averaged.

  32. 31/60 Methods for Event Selection • Cut-flow • Machine Learning • Boosted Decision Tree (BDT) 泰坦尼克号乘客能否幸存的决策树

  33. 32/60 Methods for Event Selection • Cut-flow • Machine Learning • Boosted Decision Tree (BDT) • Neural Networks • Shallow Neural Network (NN)

  34. 33/60 Methods for Event Selection • Cut-flow • Machine Learning • Boosted Decision Tree (BDT) • Neural Networks • Shallow Neural Network (NN) • Deep Learning • Deep Neural Network (DNN)1410.3469, 1402.4735, 1803.01550

  35. 34/60 Methods for Event Selection • Cut-flow • Machine Learning • Boosted Decision Tree (BDT) • Neural Networks • Shallow Neural Network (NN) • Deep Learning • Deep Neural Network (DNN) 1410.3469, 1402.4735, 1803.01550 • Convolutional Neural Network (CNN) 1708.07034 colorparticle type size energy or tansverse momentum

  36. 35/60 Pros and Cons • Cut-flow is simple but coarse, hurts signal events. • BDT can be viewed as an optimized version of cut-flow. • BDT is explainable, while NNs are hard to explain. • NNs are more powerful than BDT for non-linear mapping. • Most machine learning methods use fixed-length of inputs.

  37. Message Passing Graph Neural Network

  38. Message Passing Graph Neural Network

  39. Message Passing Graph Neural Network

  40. 39/60 Event as Graph Our Idea • Represent an event as a graph • Encode each vertex into a state vector • Message passing between vertices • Each vertex votes the signal/background • Average the votes as the final result S MP encode S S S B vote average signal or background

  41. 41/60 Performance Index Expected discovery significance is • : the number of selected signal and background events • : cross section • : integrated luminosity • : efficiencies of preselection cuts and classifier We define the expected relative discovery significance as

  42. 42/60 Detailed operation（1） Message Passing Neural Network

  43. 43/60 Neural Network Model Detailed operation （2） • Use one-hot-like encoding for object identity. • 30-dim feature vectors • Distance measure using • Pair distances are expanded in a Gaussian basis (linearly distributed in [0, 5]) as vectors of length 21. • Use separate message and update functions for each iteration. Training relu: rectified linear unit (non linear trans) expand: expand to Gausian basis to form a vector • Binary Cross-Entropy (BCE) as loss function. • Calculate gradients using error back-propagation. • Optimize network parameters using Adam algorithm. • Training with mini-batch of examples. • Adopt early stopping to prevent overfitting. ,,,: parameters ,,,: linear transformations

  44. 44/60 Search for stop-pair signal The diagonal region of phase space () is hard to hunt • The momentum transfer from to is small. • The stop signal is kinematically very similar to process.

  45. 45/60 Search for stop-pair signal Generate events with ATLAS detector • MadGraph5 + Pythia8 + Delphes3 + CheckMate2 • Signal events • Background events Object reconstruction • Electron and muon • Jet • Anti-kt clustering () • B-tagging • 80% efficiency Preselection criteria

  46. 46/60 Search for stop-pair signal encoding

  47. 47/60 An event graph with detailed node features and distance matrix, built from a Monte Carlo simulated event

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