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Comparison of MOKKA and MARLIN Outputs with BRAHMS Results for Particle Tracking Analysis

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ghalib
10 diapositives · 231 Views · Sep 19, 2014

This document explores the discrepancies observed in particle tracking results when comparing MOKKA input data against MARLIN results, particularly focusing on Z-peak analysis. It highlights significant differences stemming from multiple scattering effects simulated in full Monte Carlo (MC) setups. The study also evaluates performance variations in BRAHMS outputs due to diverse track selection methodologies. While MARLIN (MOKKA input) uses the same track selection as in previous studies, recent findings reveal unexpected degradation in performance, warranting further investigation into underlying factors.

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Comparison of MOKKA and MARLIN Outputs with BRAHMS Results for Particle Tracking Analysis
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  1. System Test with MOKKA input • currently look at Z-peak results, using the sample Ben generated on the csf farm • results look considerably different from the ones of Marlin with SGV input, due to effects only • simulated in the full MC (non-Gaussian tails on angular distributions from multiple scattering) • does not make much sense to compare to Marlin(SGV input), instead compare with BRAHMS • two BRAHMS results available, which differ due to different level of cheating, differences in • track selection etc: TESLA-TDR result and plots from LC-note LC-PHSM-2003-061 • MARLIN(Mokka input) uses same track selectionthat was used in LC-PHSM-2003-061 which was • derived from the BRAHMS sample – should be a good first approximation of what will be • needed for MOKKA, though this should be revisited after the release • in addition, in MARLIN we currently suppress K-shorts and Lambdas and switch off photon • conversions in GEANT4 • MOKKA detector has layer thickness 0.12% X0, known to considerably degrade performance

  2. The two BRAHMS results in comparison b c (b-bkgr) open: TESLA-TDR full: LC-PHSM-2003-061 c • LC-note result uses more realistic tracking and track selection derived from the • sample used; performance slightly worse than previous TESLA-TDR result • results shown for nominal layer thickness at time of TESLA-TDR of 0.064% X0

  3. MARLIN (MOKKA input) compared to BRAHMS b c (b-bkgr) c open: BRAHMS, LC-note full: MARLIN (Mokka) • compare to result from LC-note (same track selection used in MARLIN) • Marlin result falls significantly short of BRAHMS performance  look at inputs

  4. b full: MARLIN(MOKKA) line: MARLIN(SGV input) for some variables find excellent agreement for example momentum of most significant track in the jet c uds

  5. b significant difference seen in joint probability (both in R-phi, see plots, and in z) uds jets “look like” heavy flavour c uds

  6. b this is already seen at the level of the impact parameter significances that are used to calculate the joint probability c uds

  7. b c uds

  8. b also, ZVRES seems to find secondary vertices in light jets! (plots show number of tracks in non-primary vertices) c uds

  9. b this is confirmed by the resulting M_Pt distributions c uds

  10. Preliminary conclusions • confidence in the MARLIN code obtained from System Test with SGV input • continues through to System Test with MOKKA input, e.g. some input distributions • agree very well with MARLIN(SGV input) • some of the degradation seen wrt SGV-input is expected from full MC and from the • increased layer thickness used in the detector model • however there are some effects needing further investigation: • ZVRES finds larger fraction of light jets with secondary vertices compared to • cleaner SGV-events, • impact parameter significances could also give rise to confusion of light with • heavy flavour jets