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Work to Improve n e Identification

Work to Improve n e Identification. Alex Smith University of Minnesota. Overview. Migration of JM EID to TMVA framework Consistency between old and new Plans for implementation Addition of reconstructed E( n e ) to training Summary and Plans. Motivation.

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Work to Improve n e Identification

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  1. Work to Improve ne Identification Alex Smith University of Minnesota

  2. Overview • Migration of JM EID to TMVA framework • Consistency between old and new • Plans for implementation • Addition of reconstructed E(ne) to training • Summary and Plans

  3. Motivation • JM EID currently use Root’s TMultilayerPerceptron as the algorithm for multivariate analysis • TMVA (Toolkit for Multivariate Analysis) • Open-source framework • Supports many different algorithms • Nice diagnostic tools • Nice standard framework-- used by others within NOvA • If we migrate JM EID to TMVA framework, we can take advantage of these features

  4. Consistency With Previous Implementation • TMVA includes the Root TMultilayerPerceptron as one option • Run this and compare with results from Jianming’s EID • Not possible to specify exactly the same events • Must run large sample in order to compare • Monte Carlo samples • Signal: Swap MC, applied to sample in training • Background: Generic FD MC (includes beam ne)

  5. Comparison: Previous to TMVA Beam ne CC NC Background TMVA TMVA Previous Previous Efficiency Efficiency MVA Variable MVA Variable

  6. Comparison: Previous to TMVA nm CC ne CC, Wrong Particle Chosen for e TMVA TMVA Previous Previous Efficiency Efficiency MVA Variable MVA Variable

  7. Comparison: Previous to TMVA Figure of Merit Signal neCC TMVA TMVA Previous Previous Figure of Merit Efficiency MVA Variable MVA Variable

  8. Comparison: Previous to TMVA TMVA Previous Events MVA Variable

  9. Comparison: Previous to TMVA TMVA TMVA Previous Previous Events Events MVA Variable MVA Variable

  10. Comparison: Previous to TMVA TMVA TMVA Previous Previous Events Events MVA Variable MVA Variable

  11. Consistency With Previous Implementation • The TMVA implementation is consistent with the Root/TMultilayerPerceptron version of the current EID • Not possible to select exactly the same training/test subsamples from the TMVA interface • FOM is the same within uncertainties • Can move forward with this implementation of EID

  12. Plan for Implementation in NOVA Framework • We plan to provide a few different options of EID variables in the analysis framework: • Without E(ne) • Including E(ne) • Other sets of variables to be determined (suggestions?) • Different MVA algorithms (Artificial Neural Networks, Boosted Decision Trees, k-Nearest Neighbor, H-Matrix, etc.) • Ultimately, the user can select the EID variable that best suits their analysis

  13. Addition of Reconstructed E(ne) to ANN Training • We will provide EID variables both with and without E(ne) included in the training • Caution: Using E(ne) in the training will bias the E(ne) distribution • It is preferable to use MVA without E(ne) and do a 2D fit to reconstructed E(ne) and MVA discriminator if you care about the E(ne) shape • This will allow comparison with other EID packages that include E(ne)

  14. Definition of Input Variables egLLL= evtSh1DedxLLL[0] - evtSh1DedxLLL[1]; egLLT = evtSh1DedxLLT[0] - evtSh1DedxLLT[1]; emuLLL = evtSh1DedxLLL[0] - evtSh1DedxLLL[2]; emuLLT = evtSh1DedxLLT[0] - evtSh1DedxLLT[2]; epi0LLL = evtSh1DedxLLL[0] - evtSh1DedxLLL[3]; epi0LLT = evtSh1DedxLLT[0] - evtSh1DedxLLT[3]; epLLL = evtSh1DedxLLL[0] - evtSh1DedxLLL[5]; epLLT = evtSh1DedxLLT[0] - evtSh1DedxLLT[5]; enLLL = evtSh1DedxLLL[0] - evtSh1DedxLLL[6]; enLLT = evtSh1DedxLLT[0] - evtSh1DedxLLT[6]; epiLLL = evtSh1DedxLLL[0] - evtSh1DedxLLL[7]; epiLLT = evtSh1DedxLLT[0] - evtSh1DedxLLT[7]; gap = evtSh1Gap; pi0mass = Max(evtSh1Pi0Mgg, 0.0); vtxgev = evtSh1VtxGeV; shE= evtSh1Energy / evtSh1SliceGeV; nueRecEnergy = (evtSh1Energy + 0.282525 + 1.0766*(evtEtot-evtSh1Energy));

  15. Input Variables

  16. Input Variables

  17. Input Variables

  18. Comparison: With/Without E(ne) nm CC nm CC ne CC, Wrong Particle Chosen for e ne CC, Wrong Particle Chosen for e With E(ne) With E(ne) No E(ne) No E(ne) Efficiency Efficiency MVA Variable MVA Variable

  19. Comparison: With/Without E(ne) Beam ne CC NC Background With E(ne) With E(ne) No E(ne) No E(ne) Efficiency Efficiency MVA Variable MVA Variable

  20. Comparison: With/Without E(ne) Figure of Merit Signal neCC With E(ne) With E(ne) No E(ne) No E(ne) Figure of Merit Efficiency MVA Variable MVA Variable

  21. Comparison: With/Without E(ne) With E(ne) No E(ne) Events MVA Variable

  22. Comparison: With/Without E(ne) With E(ne) With E(ne) No E(ne) No E(ne) Events Events MVA Variable MVA Variable

  23. Comparison: With/Without E(ne) With E(ne) With E(ne) No E(ne) No E(ne) Events Events MVA Variable MVA Variable

  24. Importance of Variables to Separation

  25. Variable Correlations

  26. Separation Performance

  27. Other MVA Algorithms Can Be Used Boosted Decision Tree K-Nearest Neighbor • Not optimized for performance – just used default parameters • Can certainly do better

  28. Summary and Plans • JM EID training migrated to TMVA • Demonstrated that results are consistent • Working on code to implement this in NOvA analysis framework, should be available soon for others to use • Added E(ne) to training • Figure of merit (FOM) increases from 6.4 to 6.5 • Using • If use like others, FOM = ~6.8 • Investigate other variables and MVA algorithms

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