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Bridge slide comet_deep_kaneko_20200210_pub

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Slides for ``Machine Learning and Physics Bridge vol.1''

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Bridge slide comet_deep_kaneko_20200210_pub

  1. 1. 1 深層学習を活用したCOMET 実験データ解析 Fumihiro Kaneko(Saitama University alumnus) In collaboration with Uesaka, Ohashi, Saito, Sato, Jittoh, Takenouchi, Horigome, Morita (Saitama University and it’s alumni) Kuno, Wu, Wong (Osaka University)
  2. 2. 2 Outline 1.Introduction Our target COMET? and its analysis 2. Deep learning model 3. Results 4.Conclusions
  3. 3. 3 Outline 1.Introduction Our target COMET? and its analysis 2. Deep learning model 3. Results 4.Conclusions
  4. 4. 4 Target: DL × Test Data Analysis Verify whether Deep Learning is effective to high energy physics data analysis : Image Recognition Verify whether Deep Learning is effective to high energy physics data analysis : Image Recognition High energy physics data: Desired phenomena appear with a pattern on an image Deep Learning(DL): Outperforming human accuracy COMET Phase Ⅰ TDR Comet Collaboration 1812.09018 A A’ Section A-A’ ILSVRC (ImageNet Large Scale Visual Recognition Challenge) Classification Task ○1000 classes Russakovsky, O., Deng, J., Su, H. et al. Int J Comput Vis (2015) 115: 211.
  5. 5. 5 COMET実験? COMET: COherent Muon to Electron Transition      保存則の破れ``μN → eN’’の観測を通じた新物理の探索 45の機関,大学が参加する国際collaboration 実験設備:茨城県東海村のJ-PARK内      Phase-1が2022年?から稼働予定 COMET: COherent Muon to Electron Transition      保存則の破れ``μN → eN’’の観測を通じた新物理の探索 45の機関,大学が参加する国際collaboration 実験設備:茨城県東海村のJ-PARK内      Phase-1が2022年?から稼働予定 COMET Phase-Ⅰ TDR:Comet Collaboration 1812.09018 ``Charged Lepton Flavour Violation’’を探索
  6. 6. 6 COMET Phase-Ⅰ Process: muon beam → muonic atom → electron at magnetic field Process: muon beam → muonic atom → electron at magnetic field COMET Phase-Ⅰ Detector Layout
  7. 7. 7 COMET Phase-Ⅰ COMET Phase-Ⅰ Detector Layout ALμ Muonic Atom!! Process: muon beam → muonic atom → electron at magnetic field Process: muon beam → muonic atom → electron at magnetic field
  8. 8. 8 COMET Phase-Ⅰ COMET Phase-Ⅰ Detector Layout AL e Process: muon beam → muonic atom → electron at magnetic field Process: muon beam → muonic atom → electron at magnetic field
  9. 9. 9 COMET Phase-Ⅰ Each event can be expressed with a 2D image → Target process appear with circular track Each event can be expressed with a 2D image → Target process appear with circular track A A’ Detector readout A-A’ Simulation Result COMET Phase-Ⅰ TDR Comet Collaboration 1812.09018 COMET Phase-Ⅰ Detector Layout AL e AL e
  10. 10. 10 COMET Difficulties There are other processes, to form the same track and to keep the number of the lepton flavour There are other processes, to form the same track and to keep the number of the lepton flavour Simulation Result COMET Phase-Ⅰ TDR Comet Collaboration 1812.09018 Detector readout A-A’ AL e AL e AL e AL e
  11. 11. 11 COMET Data Analysis To separate the target process from others → Need high resolution for energy → track radius To separate the target process from others → Need high resolution for energy → track radius COMET Phase-Ⅰ TDR Comet Collaboration 1812.09018 The energy is determined by the track radius.
  12. 12. 12 Today’s topic Extract only the first turn hits from multiple turn events ⇛ Better “Energy Estimation” Extract only the first turn hits from multiple turn events ⇛ Better “Energy Estimation” GBDT Track Finding Turn 1 Extraction Energy Estimation Is it possible by Deep Learning?? No good results Kalmann Filter COMET Data Analysis
  13. 13. 13 Outline 1.Introduction Our target COMET? and its analysis 2. Deep learning model 3. Results 4.Conclusions
  14. 14. 14 Deep Learning: Model? Deep Learning ⇔ Deep neural network Human neuron system Stacked Layer Network with non linearity Deep Learning ⇔ Deep neural network Human neuron system Stacked Layer Network with non linearity Connection Node activation or Node Output Network Input Network Output
  15. 15. 15 Deep Learning: Learning? Parameters fitting with training loss Deep learning : Fitting with millions of images Parameters fitting with training loss Deep learning : Fitting with millions of images Training Data Set: {Input, target} pair Output Training Loss Several images Fitting or learning DL model Input Target
  16. 16. 16 Deep Learning:Model Selection Problem How to select a model correctly? 1. No manifest theory 2. Deeper or larger models are better. millions parameters. ⇛ Designing a network from scratch is not efficient. How to select a model correctly? 1. No manifest theory 2. Deeper or larger models are better. millions parameters. ⇛ Designing a network from scratch is not efficient. arXiv:1811.06965v4 Image net top-1 accuracy

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