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Impact parameter resolution ~where we stand Attilio Andreazza

Impact parameter resolution ~where we stand Attilio Andreazza. What has been presented so far. IP with unfolding (Jiri) Rel 16 data are ~5% worse than MC σ (d0) overestimate the Gaussian part: 10% in MC, 5% in data IP truth, RMS in ±3 σ (Andrea) σ (d0) agress with resolution

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Impact parameter resolution ~where we stand Attilio Andreazza

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  1. Impact parameter resolution ~where we standAttilio Andreazza

  2. What has been presented so far • IP with unfolding (Jiri)Rel 16 data are ~5% worse than MCσ(d0) overestimate the Gaussian part: • 10% in MC, 5% in data • IP truth, RMS in ±3σ(Andrea) σ(d0) agress with resolution • Primary vertex (Kirill, Andi, Simone)about 10% or more scale factors • covariance matrix underestimate the resolution • Isolated muon samples (Nicole)unbiased impact parameter resolution ~20% worse in data • similar effect on biased impact parameter resolution (Tony Shao in Z→τμτμ) /afs/cern.ch/user/q/qitek/public/Plots_data_mc_r16_r15_unf_corr_KIP/ /afs/cern.ch/user/q/qitek/public/ Plots_data_mc_r16_new / A. Andreazza, IP introduction

  3. What has been presented so far • IP with unfolding (Jiri)Rel 16 data are ~5% worse than MCσ(d0) overestimate the Gaussian part: • 10% in MC, 5% in data • IP truth, RMS in ±3σ(Andrea) σ(d0) agress with resolution • Primary vertex (Kirill, Andi, Simone)about 10% or more scale factors • covariance matrix underestimate the resolution • Isolated muon samples (Nicole)unbiased impact parameter resolution ~20% worse in data • similar effect on biased impact parameter resolution (Tony Shao in Z→τμτμ) 1.2 1 0.8 |MC Pull, 1 BL hit 1.2 1 0.8 |MC Pull, >1 BL hit https://indico.cern.ch/getFile.py/access?contribId=3&resId=0&materialId=slides&confId=136932 / A. Andreazza, IP introduction

  4. What has been presented so far • IP with unfolding (Jiri)Rel 16 data are ~5% worse than MCσ(d0) overestimate the Gaussian part: • 10% in MC, 5% in data • IP truth, RMS in ±3σ(Andrea) σ(d0) agress with resolution • Primary vertex (Kirill, Andi, Simone)about 10% or more scale factors • covariance matrix underestimate the resolution • Isolated muon samples (Nicole)unbiased impact parameter resolution ~20% worse in data • similar effect on biased impact parameter resolution (Tony Shao in Z→τμτμ) MinBias data MinBias MC https://indico.cern.ch/getFile.py/access?contribId=2&resId=0&materialId=slides&confId=136404 A. Andreazza, IP introduction

  5. What has been presented so far • IP with unfolding (Jiri)Rel 16 data are ~5% worse than MCσ(d0) overestimate the Gaussian part: • 10% in MC, 5% in data • IP truth, RMS in ±3σ(Andrea) σ(d0) agress with resolution • Primary vertex (Kirill, Andi, Simone)about 10% or more scale factors • covariance matrix underestimate the resolution • Isolated muon samples (Nicole)unbiased impact parameter resolution ~20% worse in data • similar effect on biased impact parameter resolution (Tony Shao in Z→τμτμ) https://indico.cern.ch/getFile.py/access?contribId=0&resId=0&materialId=slides&confId=136932 https://indico.cern.ch/getFile.py/access?contribId=5&resId=0&materialId=slides&confId=137517 A. Andreazza, IP introduction

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