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Background Rejection Activities in Italy Francesco Longo University and INFN, Trieste, Italy

Gamma-ray Large Area Space Telescope. Background Rejection Activities in Italy Francesco Longo University and INFN, Trieste, Italy francesco.longo@ts.infn.it On behalf of the “North-East” INFN group

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Background Rejection Activities in Italy Francesco Longo University and INFN, Trieste, Italy

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  1. Gamma-ray Large Area Space Telescope Background Rejection Activities in Italy Francesco Longo University and INFN, Trieste, Italy francesco.longo@ts.infn.it On behalf of the “North-East” INFN group thanks in particular to R.Rando, O.Tibolla, Y.Lei, G.Busetto and P.Azzi (University and INFN Padova)

  2. Bkg Rejection activity • Starting from “simple cuts” • Collection of Bkg Rejection documentation • Understanding IM cuts (DC1 variables and cuts) • Classification Trees in R • New recent developments • Ready for new data • More Info: http://sirad.pd.infn.it/glast/ground_sw/dc2.html

  3. “By hands” cuts • First iteration using already suggested cuts • Look into Merit Tuple to find efficiency of rejection and gamma acceptance • Reference docs (Atwood): • “Instrument response studies” • “Post rome background rejection” • Datasets: • DC1 prep background ntuples • DC1 prep gamma Merit nutple • Divide events in particle type: gamma(signal), gamma(bkg), electron+positron, protons

  4. Calorimeter categories • Definitions: • No cal: CalEnergySum<5. || CalTotRLn≤2 • Low Cal: CalEnergySum>5. && CalTotRLn>2 • Med Cal: CalEnergySum>350. && CalTotRLn>2 • High Cal: CalEnergySum<3500. && CalTotRLn>2 • “Good Energy” events: “good_energy” = (EvtEnergySumOpt-MCEnergy)/MCEnergy |”good_energy”| ≤ 35%

  5. Signal events split

  6. Background events split

  7. Gamma Low Cal GOOD BAD ALL Good ene High Cal ALL GOOD BAD MCEnergy

  8. “Tree” cuts • Using IM xml file in classification • Develop a “Node” structure parsing the xml IM output • Check of cuts

  9. CT approach ID predicate ID 0/1 predicate

  10. Signal events split

  11. Good Cal E

  12. Starting with Classification Trees • Use of R program – rpart (recursive partitioning) • Searching to optimize “goodCal” • For each step rpart reports the cost-complexity of the tree, the number of splits, the relative error and finally the error that it obtains from a process of cross validation, with the corresponding sigma.

  13. Classification Trees with rpart

  14. Classification Trees with rpart

  15. Classification Trees with rpart

  16. Classification Trees with rpart

  17. One Tree per E bin

  18. One Tree per E bin

  19. One Tree per E bin

  20. Classification Trees with R

  21. rpart Classification

  22. rpart Classification

  23. Error costs

  24. Error Costs

  25. Error Costs

  26. Random Forest

  27. Random Forests

  28. Random Forests

  29. Random Forests

  30. rForest package

  31. Conclusions • Work is progressing… • Work on new variables started • More results will come…

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