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Efficient Zero Suppression Study for Fast Monte Carlo Simulation in Jet Energy Profiling

Explore a detailed study conducted by John Krane at Iowa State University in 2002 on optimizing fast Monte Carlo simulation for jet energy profiles using innovative suppression techniques and noise simulation strategies. Learn about the goals and methods employed, as well as the simulation results and analysis findings. Discover the impact of ZSP schemes and noise characteristics on dijet resolutions.

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Efficient Zero Suppression Study for Fast Monte Carlo Simulation in Jet Energy Profiling

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  1. Zero Suppression Studywith a fast MC John Krane Iowa State University Calorimeter Task Force 1 October, 2002

  2. Goals • Run 1k events in a few seconds(actual is currently 15k jets/min) • Simulate jet energy profiles • Longitudinal • Ncells in each layer • Simulate noise • Simulate arbitrary ZSP scheme John Krane -- Iowa State University

  3. Method • Study 100 GeV jets and scale down • Relatively free of noise pathology • Copious • Get Ncells per layer (Draw from Gaussian from data) • Get Efrac for each cell in lyr(Draw from histogram of E/cell in each layer, from data) • Get energy per layer for CC jets(Draw from histogram of E/lyr from data,scale this down) John Krane -- Iowa State University

  4. Method, continued • Add noise (Robert’s .rcp provides sigmas) to jet cells and then suppress • Add noise to all other cells in jet cone and then suppress • Suppression can be 1.5s, 2.5s, or different s for different layers My noise is slightly asymmetric…also I am using negative E John Krane -- Iowa State University

  5. Simulated jets have medium ET, mean is 47 GeV, mode is ~40 GeV (Could make ETcuts but don’t) John Krane -- Iowa State University

  6. Several GeV consist entirelyof noise …but most jets have zero noise (2.5 s zsp) John Krane -- Iowa State University

  7. Jet Reso 2.5 s zsp requiressmaller correctionfor noise …but note the asymmetric shape! Results in a long tailon dijet resolutions difficult analysiscould result from myasymmetric noise John Krane -- Iowa State University

  8. ZSP schemes • 1.5 s zsp • 2.5 s zsp • 1.5 in low-noise layers (2,3,4,5,6,7)and 2.5 elsewhere • 1.5 in low-occupancy layers (12,13,15)and 2.5 elsewhere John Krane -- Iowa State University

  9. Cut hard on noisy layers More reso First look indicatesthe blue line is fairly Gaussian in shape, but this might be a fitting oddity…will look more carefully Cut hard on populous layers John Krane -- Iowa State University

  10. Zsp choice makeslarge diff in MC Blue line is a clever(i.e. counter-intuitive)choice for reso, andnaturally good for the occupancy John Krane -- Iowa State University

  11. CHF Started checking to see if jet cut variables looked familiar… John Krane -- Iowa State University

  12. Summary • Fast ZSP Monte Carlo is working • Will study jet variables to check accuracy • Can improve noise sim with direct histograms • Ignoring possible correlation between E and Ncells • Could try different ET regime, h ranges • Need suggestions for ZSP schemes • Tried two variable-s schemes (suppress noisy layers, suppress high-occupancy layers) • Decide what variables to optimize (How should I simulate the MET? Systematic noise?) John Krane -- Iowa State University

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