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Impact parameter resolution ~where we stand Attilio Andreazza

Discover the latest developments in improving impact parameter resolution in high-energy physics research, addressing issues such as underestimated covariance matrices and unbiased impact parameters. Learn about comparisons between data and MCσ(d0) results and the impact of primary vertices on scale factors.

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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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