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Experimental Particle Physics

Understanding the measurements and searches at the Large Hadron Collider
Deepak Kar


Experimental Particle Physics is written for advanced undergraduate or beginning postgraduate students starting data analysis in experimental particle physics at the Large Hadron Collider (LHC) at CERN. Assuming only a basic knowledge of quantum mechanics and special relativity, the text reviews the current state of affairs in particle physics, before comprehensively introducing all the ingredients that go into an analysis. It encompasses how researchers go from electronic signals in the detectors to visualising what particles were produced in a collision, and how we determine if that is consistent with Standard Model predictions or indicates the presence of yet unseen particles. The results are usually represented in what we call plots, and this book ensures students can understand what goes into the making of the plots as well as how to interpret them. An overview of jet substructure techniques from an experimentalist's perspective is also provided, along with a short introduction to modern machine learning methods, which focusses on its application in particle physics data analysis. The exercises at the end of each chapter are designed to ensure the student is aware of the common pitfalls encountered during data analyses.

About Editors

Deepak Kar is an associate professor at the School of Physics at the University of Witwatersrand, South Africa. He obtained his PhD from the University of Florida in 2008 working on the CDF experiment at Tevatron in Fermilab. Previously, he was a post-doctoral researcher at the University of Glasgow as well as the Technische Universitsät, Dresden, and he worked as a member of the ATLAS collaboration at the Large Hadron Collider at CERN.

Table of Contents

1 Groundwork

2 Collisions

3 Analysis objects

4 Theoretical view of collisions and simulating them

5 Analysis

6 Uncertainties 

7 Presenting and interpreting the results

8 Advanced topic: jet substructure

9 Advanced topic: machine learning


Hardback ISBN: 9780750321105

Ebook ISBN: 9780750321129

DOI: 10.1088/2053-2563/ab1be6

Publisher: Institute of Physics Publishing


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