CMSDAS Pisa 2019 Muon Object (Short Exercise)

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Contacts - Facilitators

Abstract

This exercise aims at getting attendees familiar with muon object reconstruction, and with the use of muons in CMS analyses in general:

  • a pedagogical introduction on reconstruction, identification, isolation and muon momentum assignment will be presented at the beginning of the exercise
  • a hands-on session will follow, covering:
    • basics about how to access muon objects, as well as their identification and isolation variables
    • examples of how to assess the performance of the muon reference identification criteria and isolation for signal and background muons in montecarlo
    • highlights about the measurements of muon identification efficiency in data
    • highlights on the use of muon momentum scale and resolution corrections (T.B.C.)

Prerequisites / synergies

The attendees are assumed to have basic knowledge of:

Some familiarity with Git (and GitHub) is helpful, but not strictly necessary.

This tutorial does not require any other short exercise as pre-requisite, anyhow synergies can be identified with the Tracking and Primary Vertices and, in part, with the Particle Flow exercises.

Miscellaneous notes

The color scheme used for the exercise is the following:

  • Shell commands are embedded in grey box, e.g. :
    ipython -i muonIdAnalysis.py
  • Output and screen printouts are embedded in green box, e.g.:
    "[tnpIdAnalysis::Loop] processed : 10000 entries"
  • General code snippets are embedded in red box, e.g.:
    print "\tmuon:", iMu, mu.charge(), mu.pt(), mu.phi(), mu.eta()
  • Questions to be answered by attendees and important messages will be highlighted in green

0. Introduction

Slides

A brief talk outlining of the basics muon object reconstruction, identification, isolation and momentum assignment will be presented before the hands-on session.

Setting up the working area

1. Muon identification and isolation in MC

Accessing muon object information

Muon identification performance

Muon isolation performance

2. Highlights on muon identification performance with real data

Principles of the tag-and-probe analysis

An example of high-level calibration: data/MC efficiency scale-factors

3. Muon momentum scale and resolution corrections (T.B.C.)

Appendix

Publications on muon reconstruction with LHC collision data

Publications on muon reconstruction with LHC collision data

-- Main.Carlo.Battilana - 2010-01-10

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Topic revision: r5 - 2019-01-11 - CarloBattilana
 
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