Learning Particle Physics by Example:

Transcription

1 Learning Particle Physics by Example: Accelerating Science with Generative Adversarial Networks Luke de Oliveira Partner, Manifold Visiting Researcher, Lawrence Berkeley National Lab S7666, May 11th, GPUTech 2017

2 Outline High Energy Particle Physics / LHC Physics How do we probe phenomena in the physical sciences? What problems does this introduce? Location Aware GANs / CaloGANs Applications to other domains

3 Why this matters GANs provide a viable strategy for speeding up numerically intensive simulation - extends to other disciplines (medicine, weather, nuclear) Basic science provides theory-driven success metrics feedback loop for pure ML research

4 High Energy Physics at the Large Hadron Collider

5 LHC 27 km ring at the border of Switzerland and France Accelerate bunches of protons at % the speed of light - approx. 90μs per lap 14TeV collision energy let s us probe constituents, investigate rare decays, examine new theories (supersymmetry, extra dimensions, gravitons) Many experiments: ATLAS, CMS, LHCb, ALICE,

6 What does a proton-proton collision look like?

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8 What is a Particle Detector? ATLAS detector is a 3D camera with a temporal component that measures energy deposition in different materials and in different environments. What are we measuring? What does it look like?

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10 Acquiring Labels Unlike many fields we ve seen here at GPUTech, we can t label data in High Energy Physics The most brilliant physicist cannot look at a detector read out and say what happened Use a constructivist approach, start with theory, then simulate (we know labels from simulation)

11 Finding Physics using Simulation To perform searches for new physics or precision measurements of known particles we simulate billions of collisions Allows us to calculate yield, discovery significance, build classifiers This is very hard - we always have all problems associated with transfer learning

12 High Energy Physics Simulation Encode infinite-dimensional integrals, full Quantum Field Theory into Monte Carlo Simulation Using approximations, can bound the time required Can we do better?

13 Simulation Overview QFT / Theory Simulate proton-proton interactions Simulate showering Interaction with detector materials / geometry

14 Simulation Overview QFT / Theory Simulate proton-proton interactions Simulate showering CPU intensive (~60% of worldwide computing grid) Interaction with detector materials / geometry

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16 Why is this hard? This is the first series of layers. We need to model interactions on the to resolve showering of particles

17 Trifecta of Issues Full Simulation is slow Detector simulation can take minutes per event Time Hard to model tailstatistics To get a few rare events in kinematically unfavorable regions, we often have to simulate a large number of events Petabytes of Simulated Data Large amounts of simulated data needs to be stored and transferred Disk Space Rare Events

18 Our Panacea Fast Portable Specialized

19 Our Panacea Fast Portable Specialized GANs?

20 Location Aware Generative Adversarial Networks arxiv:

21 Generative Adversarial Networks (GAN) Turn generative modeling into a two player, non-cooperative game. tries to tell fake/real tries to produce real looking samples

22 Defining a Step 0 - Jet Images Avoid producing full event, focus on jets - cone shaped collections of hadrons (e.g., electrons) forming showers η = 0 η = - η = +

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24 Defining a Step 0 - Jet Images Key insight - unroll detector, well behaved manifold [arxiv/{ , }]

25 From Particle Physics Computer Vision Jet Images are 2D images where pixel intensity is transverse momentum (component perpendicular to proton path) Single Jet Image Mean of 10 4 Jet Images

26 Step 0 Task Generate Jet Images using traditional software (PYTHIA) Learn a generative model to reproduce jets with high fidelity Fidelity defined as ability to reproduce physics quantities (manifolds) such as mass, other theoretically motivated quantities which we don t have for most generative tasks Provide a speedup over traditional generation

27 Defining Characteristics Large (5 orders of magnitude) dynamic range Sparse (7%) occupancy of images Changing location of 1 pixel activation wildly different characteristics in spacetime Important features have high Lipschitz constant

28 Location Aware GAN (LAGAN) Joint work with Michela Paganini (Yale) and Benjamin Nachman (Lawrence Berkeley National Lab) arxiv: Design domain-specific tweaks and modifications to GAN architecture Use AC-GAN variant with 2 classes QCD (background) and W bosons (signal) well understood from Physics

29 LAGAN

30 Architectural Details Latent space: Perform Hadamard product between latent space and lookup table for conditioning (AC-GAN) Rely on locally connected building blocks (i.e., convolutions without weight sharing) to leverage spacetime symmetries

31 LAGAN guidelines for sparse data Activation functions Batch Normalization Leaky ReLU (rec. in GAN lit.) ReLU (chosen to induce sparsity) Stabilize gradients, help with HDR Minibatch Discrimination

32 Qualitative Assessment 5 random Jet Images nearest LAGAN-generated neighbor GAN-generated signal - background Real signal - background

33 Checking Against Theory Can a GAN recover these distributions without being trained on them?

34 Checking Physical Properties n-subjettiness jet mass

35 Our Solution Fast Portable Specialized

36 Our Solution Currently used benchmark Fast Our solution

37 Our Solution We condition on particle class, but can easily condition on mass, momentum, Specialized

38 Our Solution Trained LAGAN weights: 20 MB Source code: 480 kb Portable

39 Can we go one step further? We ve shown that we can perform showering Can we simulate how positron / pion / photon showers interact with materials? Most expensive part of simulation. Large simulation package, GEANT4, can take 5 mins per electron to generate its interaction with a detector.

40 CaloGAN arxiv:

41 Solution: CaloGAN Simulate subatomic particles interacting with calorimeter media Shoot charged pions, positrons, and photons at cells of super-cooled liquid argon, use GAN to simulate what happens

42 How does an electron look in liquid argon? 2D slice η direction [mm] Geant4, Pb Absorber, lar Gap, 10 GeV e Local Energy Deposit [MeV] η direction [mm] Geant4, Pb Absorber, lar Gap, 10 GeV e Cell Energy [MeV] Depth from Calorimeter Center [mm] Depth from Calorimeter Center [mm] 0 We simulate exact (x, y, z) What we can read out is this

43 How does an electron look in liquid argon? 3 layers, unequal resolution

44 CaloGAN Architecture Condition on Energy - express conservation of energy as a approx. sub-differentiable constraint Same principles as LAGAN Jointly train all three layers of a calorimeter

45 CaloGAN Generator Three independent streams, one per calorimeter layer Custom attention mechanism decides how much from one layer to carry to the next layer

46 CaloGAN Generator

47 Nearest Generated Samples (Positrons) Real 1st layer deposition CaloGAN 1st layer deposition Real 2nd layer deposition CaloGAN 2nd layer deposition Real 3rd layer deposition CaloGAN 3rd layer deposition

48 Matching Physical Features Faithfully reproduce energy-per-layer

49 Conditioning on Energy

50 GPU Based Speed-up Up to a 10 5 speed-up!

51 Other Applications Recent successes with GANs in Cosmology Interest in fluids, weather, medicine (nuclear) Nuclear Physics (Pheno) Combustion

52 The Future Generative Models, GANs in particular, show promise for modeling exotic scientific / engineering phenomena Let s collaborate at Berkeley Lab and Manifold, we re interested in practical uses of GANs and Machine Learning in general

53 Reproducible Research We have open-sourced our code, dataset, and analysis procedure for both works.

54 Thanks!