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MARS Stuff

MARS Stuff. Curtis Lansdell University of Maryland. Outline. “Standard” Discrimination MARS, Neural Networks, and x2 MARS Distributions and Q-factors MARS and the Crab GEANT 4. Eyeball Discrimination. p. x2. . proton. gamma. AS+ORCOM. Old way to differentiate – x2 parameter

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MARS Stuff

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  1. MARS Stuff Curtis Lansdell University of Maryland Milagro Collaboration Meeting

  2. Outline • “Standard” Discrimination • MARS, Neural Networks, and x2 • MARS Distributions and Q-factors • MARS and the Crab • GEANT 4

  3. Eyeball Discrimination p x2  proton gamma

  4. AS+ORCOM • Old way to differentiate – x2 parameter • x2  nb2/mxPE • x2 > 2.5 yields Q ~ 1.7 using just AS layer (and ORCOM) • Retains 51%  and 8.5% hadron using 50 PMT trigger R. Atkins et al., ApJ 595, 803 (2003).

  5. Some Checks on MARS • Neural Networks, MARS should get the same or higher Q-factors than x2 • ANN (run by Xianwu) and ROOT NN package had problems recreating Q-factor from single variable (x2) • ANN and ROOT NN also had problems getting Q-factors as high as MARS for multiple variables. • MARS gives Qs closer to x2 MARS ROOT NN

  6. Multilayer+Gaussian Fitters • MARS1 (Multivariate Adaptive Regression Splines) • Should be able to determine the best parameters • Provides probability of being signal: ln[P()/P(p)] • More positive means more -like • on-pond off-pond Relative Variable Importance not in Crab REC data 1J. Friedman, “Multivariate Adaptive Regression Splines”, Annals of Statistics 19 (1991).

  7. Event Efficiencies 12 par model (for Crab) 16 par model on-pond on-pond off-pond off-pond

  8. Crab Results • Elapsed time = 416 days • Bin size = 1.3° • nAS > 55, nFit > 80 (multi-layer fits) • MARS used 12 par model (dAngle < 0.7 for gammas)

  9. GEANT 4 • Energies from 30 GeV to 100 TeV • Thrown out to 1 km and flat in radius • ~70 M proton events created so far • 4.4 k events trigger with nFit > 80 (7.8 k for nFit > 5) • Distributions still don’t match data well • Even with correct quantum efficiencies

  10. Continuing Onward… • MARS in conjunction with multilayer fitter appears to result in better discrimination in MC events. • Compared with neural network • Used ROOT NN package, but saw worse discrimination – maybe more tuning of hidden layers/nodes will help • Used 12 parameter MARS value cuts on Crab data • Worse significance than x2 > 2.5 (nFit > 80, multi-layer fits) • Oops, forgot to do energy weighting (used 2.4) • GEANT 4 events being produced • A few strange numbers need to be examined from the output • Will check more MC-data agreement • Run MARS with new MC! • Need more proton triggers and start creating gamma events

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