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Pileup Mitigation with Machine Learning (PUMML) BOOST 2017 Eric M. Metodiev Center for Theoretical Physics, Massachusetts Institute of Technology Work with Patrick T. Komiske, EMM, Benjamin P. Nachman, Matthew D. Schwartz July 19, 2017 Eric M.

  1. Pileup Mitigation with Machine Learning (PUMML) BOOST 2017 Eric M. Metodiev Center for Theoretical Physics, Massachusetts Institute of Technology Work with Patrick T. Komiske, EMM, Benjamin P. Nachman, Matthew D. Schwartz July 19, 2017 Eric M. Metodiev (MIT) PUMML July 19, 2017 1 / 23

  2. Overview Pileup Jet Images PUMML framework Performance Eric M. Metodiev (MIT) PUMML July 19, 2017 2 / 23

  3. Pileup Eric M. Metodiev (MIT) PUMML July 19, 2017 3 / 23

  4. Pileup Pileup problem in context Presently: ∼ 20 pileup vertices per bunch crossing Run 3: ∼ 80 pileup vertices per bunch crossing HL-LHC: ∼ 200 pileup vertices per bunch crossing Eric M. Metodiev (MIT) PUMML July 19, 2017 4 / 23

  5. Mitigation Approaches Pileup Per Particle Identification (PUPPI) Bertolini, Harris, Low, and Tran, arXiv:1407.6013 Correct particle/calorimeter energies based on surrounding charged pileup distribution. SoftKiller Cacciari, Salam, Soyez, arXiv:1407.0408 Dynamically determined transverse momentum cut. Jet Cleansing Krohn, Low, Schwartz, Wang, arXiv:1309.4777 Rescaling subjet four-momenta using charged leading vertex/pileup information. Used default parameters to give sense of performance. Eric M. Metodiev (MIT) PUMML July 19, 2017 5 / 23

  6. Machine Learning? How to input the information? The spirit is to organize all of our available local information. Have information on whether charged particles are pileup or not. Need low-level inputs. What sort of architecture? Use tools from modern machine learning. Don’t necessarily have to go “deep” What sort of loss function? Eric M. Metodiev (MIT) PUMML July 19, 2017 6 / 23

  7. Jet Images Treat the detector as a camera and energy deposits as pixel intensities. Cogan, Kagan, Strauss, Schwartzman. arXiv:1407.5675 Make use of the extensively developed computer vision technology, such as convolutional neural nets. de Oliviera, Kagan, Mackey, Nachman, Schwartzman. arXiv:1511.05190 Translated Translated Eric M. Metodiev (MIT) PUMML July 19, 2017 7 / 23

  8. Modern ML in HEP An overview of recent machine learning applications with jet images. Classification W vs QCD jets. (de Oliviera, Kagan, Mackey, Nachman, Schwartzman. arXiv:1511.05190 ) Top vs QCD jets. (Kasieczka, Plehn, Russell, Schell. arXiv:1701.08784 ) Quark vs Gluon jets. (Komiske, EMM, Schwartz. arXiv:1612.01551 ) And more... Generation Generative model. (de Oliveira, Paganini, Nachman. arXiv:1701.05927 ) Regression This work. For the first time! Eric M. Metodiev (MIT) PUMML July 19, 2017 8 / 23

  9. Our Model Inputs: three-channel RGB “pileup image” red = p T of all neutral particles green = p T of charged PU particles blue = p T of charged LV particles Output: single-channel neutral image output = p T of neutral LV particles Eric M. Metodiev (MIT) PUMML July 19, 2017 9 / 23

  10. Our Study Process Leading vertex: 500GeV scalar to dijets with Pythia8 R = 0 . 4 anti- k T jets in | η | < 2 with p T > 100 GeV. Pileup: NPU=140 Poissonian of soft QCD events overlaid. Image parameters: Charged jet image pixel resolution: ∆ η × ∆ φ = 0 . 025 × 0 . 025 Neutral jet image pixel resolution: ∆ η × ∆ φ = 0 . 1 × 0 . 1 Jet image size 0 . 9 × 0 . 9 Leading vertex/pileup information for charged particles with p T > 500 MeV Eric M. Metodiev (MIT) PUMML July 19, 2017 10 / 23

  11. Pileup Images Neutral Total p T Charged Pileup p T Azimuthal Angle Azimuthal Angle Pseudorapidity Pseudorapidity Charged Leading Vertex p T Neutral Leading Vertex p T Azimuthal Angle Azimuthal Angle Pseudorapidity Pseudorapidity Eric M. Metodiev (MIT) PUMML July 19, 2017 11 / 23

  12. Pileup Images Eric M. Metodiev (MIT) PUMML July 19, 2017 12 / 23

  13. Architecture What sort of neural network layers should we use? Dense: Units connected to every input pixel with different weights Locally connected: Units connected to local input patches with different weights Convolutional: Units connected to local input patches with weight sharing Eric M. Metodiev (MIT) PUMML July 19, 2017 13 / 23

  14. Architecture Architecture: Two convolutional layers 6 × 6 filter sizes 10 filters per layer Only 4711 parameters Architecture is local : Pileup removal of a pixel depends only on the information in a window around it Can apply the trained model at the event-level, jet level, or on any specified region Eric M. Metodiev (MIT) PUMML July 19, 2017 14 / 23

  15. PUMML Framework Eric M. Metodiev (MIT) PUMML July 19, 2017 15 / 23

  16. Subtracted Jets An example event with pileup and subtracted with each method. Loss function: Should we treat all p T errors equally or penalize hard/soft errors more? Eric M. Metodiev (MIT) PUMML July 19, 2017 16 / 23

  17. Subtracted Observables Distributions before and after subtraction of jet p T and dijet mass Eric M. Metodiev (MIT) PUMML July 19, 2017 17 / 23

  18. Subtracted Observables Distributions before and after subtraction of jet mass and N 95 . Eric M. Metodiev (MIT) PUMML July 19, 2017 18 / 23

  19. Subtracted Observables Distributions before and after subtraction of two energy correlation functions. Eric M. Metodiev (MIT) PUMML July 19, 2017 19 / 23

  20. Model Robustness 1.00 1.00 0.98 0.98 Jet Mass, Correlation Coefficient Jet Mass, Correlation Coefficient 0.96 0.96 0.94 0.94 0.92 0.92 0.90 0.90 PUMML 0.88 PUMML 0.88 PUPPI PUPPI 0.86 SoftKiller SoftKiller 0.86 0 50 100 150 0 20 40 60 80 100 120 140 160 180 NPU NPU Train on NPU=140 Poissonian Train on wide range of NPUs and test on different fixed-NPU uniformly in 180 and test on samples. differed fixed-NPU samples. Eric M. Metodiev (MIT) PUMML July 19, 2017 20 / 23

  21. Learning from Data Training from simulation risks mis-modelling issues Prefer to train on data rather than simulation Data overlay approach using minimum bias and zero-bias events already used by experimental groups in other contexts. Promising for training PUMML directly with data for the relevant application. Eric M. Metodiev (MIT) PUMML July 19, 2017 21 / 23

  22. Concluding Remarks We have developed an ML framework that successfully organizes all of the availabe local information to directly learn to mitigate pileup. Can use tools from modern machine learning without going “deep”. Pileup mitigation can be a good proving ground for modern machine learning techniques in high energy physics. Eric M. Metodiev (MIT) PUMML July 19, 2017 22 / 23

  23. The End Thank You! Eric M. Metodiev (MIT) PUMML July 19, 2017 23 / 23

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