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UW classification: new background rejection trees

UW classification: new background rejection trees. The eight selections (from Bill). The prefilter cuts. Implementation in merit. file structure. Each tree is described by two files: dtree.txt – ascii file with a list of weighted trees and nodes:

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UW classification: new background rejection trees

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  1. UW classification:new background rejection trees T. Burnett

  2. The eight selections (from Bill) T. Burnett

  3. The prefilter cuts T. Burnett

  4. Implementation in merit file structure • Each tree is described by two files: • dtree.txt – ascii file with a list of weighted trees and nodes: • tree: specify the weight to assign to the tree • branch: variable index, cut value • leaf: purity • variables.txt – list of the corresponding tuple variables • Evaluation is by passing a vector of floats, ordered according to the variable list. • Proposal to incorporate the prefilter cut in the tree description T. Burnett

  5. Training details • Weight signal and background to be the same • Train on the EVEN events, with optional boosting • Test with ODD events • Save training and testing efficiency curves T. Burnett

  6. Boosting: what does it do? • Nice interpolation for low background • Not much improvement in actual separation (so far) T. Burnett

  7. Preliminary single-tree background T. Burnett

  8. What about the energy resolution?Validity fractions T. Burnett

  9. The fraction of time each estimate is best T. Burnett

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