Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 Prospects of the Multivariate B Quark Tagger for the Level 2 Trigger at D   D  ’s level 1 and 2 trigger system  Heavy quark jet parameterization on level 2  Multivariate analysis methods: neural networks vs. support vector regression  Summary Axel Naumann University of Nijmegen and NIKHEF D  Collaboration

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 D  ’s Level 1 and 2 Trigger System Level 1 Trigger input rate (interaction rate): 8MHz Calorimeter, tracking system and muon information Level 2 Trigger input rate: 10kHz, output rate: 1kHz Two stages: 1.Preprocessing L1 data: Formatting and sorting, e.g. Silicon Microstrip Tracker data preprocessed by silicon track trigger (L2STT), offline impact parameter resolution 18  m 2.Global: correlations between sub-detectors, e.g. b quark tagger within about 5-10k instructions

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 D  ’s Level 1 and 2 Trigger System Data available for L2 Global e.g.: 2D track data ( r-  ), P t, impact parameters Muon, electron identification 3D Jet data, energy Jet Tracks r-  z Impact Parameter

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 L2 Heavy Quark Jet Parameterization Goal: Separation of light and heavy quark jets, e.g. for identification of H  Z b b, Z  b b Only feasible due to L2STT’s impact parameters (as b mesons have a non-negligible lifetime) Introducing “physics” parameters already on level 2: impact parameter, lepton tags, invariant mass etc.

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 L2 Heavy Quark Jet Parameterization Combining parameters - Coarse information - Very similar parameter distributions for signal and background - Very limited processing time + Multiple parameters = Increase separation power and speed using multivariate algorithm

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 Multivariate Analysis Methods Support Vector Regression vs. Neural Networks Both construct target function from training sample SVR guarantees optimal target function (“global minimum”) by design (based on a given training sample and axioms from learning theory) NNs often outperformed by SVR Criteria for selection of algorithm based on preliminary trigger simulation Abilities to separate signal from background Speed Stability SVR information: Neurocomputing journal, special issue on SVR

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 Multivariate Analysis Methods NN shows better separation abilities than SVR SVR outputNN output log (Entries)

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 Multivariate Analysis Methods Correlation of NN with SVR output shows comparable performance SignalBackground

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 Multivariate Analysis Methods Difference in algorithmic approach NN has fixed geometry, optimizes weights SVR optimizes weights and size (with fixed layout), where size is mainly defined by ease of separation of signal and background Timing studies for testing on a trained machine (target function) Processing time t(SVR)  500 * t(NN) SVR grows very large, as problem is almost inseparable Large SVR results in long processing time

Axel Naumann, DØ University of Nijmegen, The Netherlands 04/20/2002 APS April Meeting 2002 Summary D  ’s detector and trigger framework allow for sophisticated, physics oriented triggering Multivariate algorithms support complex yet fast online analyses Combining both should increase chances to see interesting heavy quark events