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Improving STT Parameters Using SVM and Jet Analysis in MC Data Generation

This document discusses the analysis of pre- and post-STT parameters for improving particle identification in high-energy physics data. It highlights the challenges faced with track and jet binning, detailing the need for a robust suppression mechanism and the application of decay length analysis (DLA). The text also examines the use of Support Vector Machines (SVM) in processing Monte Carlo generator data to enhance efficiency and purity in identifying particle behavior across various jet configurations. Relevant issues such as track density and correlation metrics are also explored.

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Improving STT Parameters Using SVM and Jet Analysis in MC Data Generation

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  1. L2 B Tagging • Bad news: Pre-STT parameters • Generator level • L2 trigsim • Good news: STT parameters • Purity vs. Efficiency • Correlation • Good news: SVM B ID meeting

  2. MC Data – Generator Level • Pythia • b b-bar incl. / QCD incl. • 2000 events each • 0 min bias • p08.10 B ID meeting

  3. MC Data – L2 “trigsim” • Pythia, Pt > 20 • b b-bar incl. / QCD incl. • 3260 events / 5000 events • 0 min bias / 0.5 min bias • mixture • p10.06.01 • Data dumped by filter as l2 analyze packages broken B ID meeting

  4. Pre-STT parameters • Generator level B ID meeting

  5. Pre-STT parameters • L2 “trigsim” data B ID meeting

  6. Pre-STT Parameters • Not feasible. • Main problems: • small number of tracks / jet • binning of tracks’ phi (160 segments) • Jet’s eta binned into 0.05 eta segments, only Jet Et known, large error on Jet E for high eta jets • Only way out: STT • DCA parameters have much higher suppression power (about 1 OoM) B ID meeting

  7. STT Parameters • Decay length • Axial tracks (within 1.6 cone) projected onto jet theta • Sum over all distances primary vertex - track / jet intersection Purity Efficiency B ID meeting

  8. STT Parameters • Ratio of tracks with high decay length • Number of tracks within 1.6 cone and LD > 10mu (OoM of STT resolution) divided by number of tracks within 1.6 cone B ID meeting

  9. STT Parameters • Decay Length Area: • Areas of the triangle spanned by • Primary vertex • PCA • Intersection of track and jet • Summed over all tracks with positive DL and relative angle < 1.6 Jet DLA Track Primary Vtx B ID meeting

  10. STT Parameters • Decay Length Area: • Track with spatial error + small relative angle can result in huge DL, but small DLA • Track with spacial error + high relative angle results in precise DL, big DLA • Relatively independent of jet axis errors • Phi: no error if as many tracks “left” of jet as “right” • Eta: Projected • Track’s DCA and phi resolution well known, and so is DLA’s. B ID meeting

  11. STT Parameters • Decay Length Area B ID meeting

  12. STT Parameters • Correlations x: DLA y: DLA x: DL y: DLR x: DLA y: DLR B ID meeting

  13. SVM • SVM output for MC Generator pre-STT parameters • 3400 jets in training sample (approx 50% QCD) • 1000 jets in test sample (50% QCD) • Non-standard implementation of SVM: Improvement by using gaussian kernel with parameter dependant width B ID meeting

  14. SVM • SVM output for MC Generator pre-STT parameters B ID meeting

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