Introduction to Physics Objects in CMS

Introduction

Overview

Teaching: 5 min
Exercises: 0 min
Questions

  • What are known as physics object in CMS?

  • What will I need to do in this lesson?

Objectives

  • Understand what physics objects are

  • Understand the dynamics of this reading assignment

Overview

The CMS is a giant detector that acts like a camera that “photographs” particle collisions, allowing us to interpret their nature.

Certainly we cannot directly observe all the particles created in the collisions because some of them decay very quickly or simply do not interact with our detector. However, we can infer their presence. If they decay to other stable particles and interact with the apparatus, they leave signals in the CMS subdetectors. These signals are used to reconstruct the decay products or infer their presence; we call these, physics objects. These objects could be electrons, muons, jets, missing energy, etc.; but also lower level objects like tracks. For the current releases of open data, we store them in ROOT files following the EDM data model in AOD format.

In the CERN Open Portal(CODP) site one can find a more detailed description of these physical objects and a list of them corresponding to 2010 and 2011/2012 releases of open data.

What is expected in this lesson?

In this lesson we will guide you through reading some documentation, which will give you a better idea about the physics objects in CMS. As you progress with the next episodes, you will find a set of simple questions that need to be answered in the corresponding questionary in our assignment form. The question numbers in the form match the numbers in this lesson. You can edit this form at any time.

Key Points

  • Basic information about physics objects in CMS can be found in the Cern Open Data portal site. A set of questions need to be answered in our assignment form.

The CMS detector

Overview

Teaching: 20 min
Exercises: 0 min
Questions

  • What are the main components of the CMS detector?

Objectives

  • Learn the very basic principles of CMS subdetectors and the technologies they use for sensing particles.

Subdetectors and their technologies

Physics objects are built with the information collected from the sensors of the CMS detector. Take a look at the CMS experiment in the image below. In this video you will get a quick feeling of how it looked back in 2018, the last year of data taking in Run 2.

Please read this short description of the CMS experiment and answer the questions below in our assignment form. Do not be afraid of exploring the details of each subdetector in the dedicated links that appear therein. They are very short.

Question #1: What is the heaviest component of the CMS Experiment?

Question #2: Where can muon tracks be measured in the CMS detector?

Question #3: According to this CMS detector summary, around how many crystals can be found in the barrel section of the ECAL subdetector?

Key Points

  • The CMS detector is a complex machine with multiple subsystems and technologies dedicated to identify/build physics objects.

Egamma and other physics objects

Overview

Teaching: 15 min
Exercises: 0 min
Questions

  • What are the key aspects in the building of electromagnetic (egamma) and some other physics objects?

Objectives

  • Learn the basic notion of egamma (electron and photons) object reconstruction.

  • Read a little bit about the reconstruction of other objects

Finding electrons and photons with the CMS detector

In this recent article you can learn about the most basic ideas of electrons and photons reconstruction. It refers mostly to the Run 2 epoch, but worry not because the essential aspects were also true for Run 1 and the open data from that epoch. Read this article and answer the following question in our assignment form.

Question #4: What is known as an electromagnetic shower in CMS?

This was an example of the many objects reconstructed in CMS. In the next episode you will dig a bit deeper into the main reconstruction algorithm used in this experiment.

Other physics objects

To expand your familiarity with physics objects reconstruction, we recommend these short reads on jets and muons.

Key Points

  • Egamma physics objects are built using information from the tracking system as well as the ECAL subdetector. It is a vivid example of physics objects reconstruction.

The Particle Flow algorithm and reconstruction of other objects

Overview

Teaching: 20 min
Exercises: 0 min
Questions

  • What is the particle flow algorithm?

  • What are the key aspects to remember about how PF works?

  • How can PF can be used to build physics objects?

Objectives

  • Learn the basic aspects of the PF algorithm.

  • Get an idea of how physics objects are built using the PF algorithm

Overview

In the last episode, you learned about how electromagnetic objects are reconstructed using methods like clustering and linking of tracks with ECAL energy deposits. These actions are essential parts of the so-called Particle Flow (PF) algorithm.

The particle-flow algorithm aims at reconstructing and identifying all stable particles in the event, i.e., electrons, muons, photons, charged hadrons and neutral hadrons, with a thorough combination of all CMS sub-detectors towards an optimal determination of their direction, energy and type. This list of individual particles is then used, as if it came from a Monte-Carlo event generator, to build jets (from which the quark and gluon energies and directions are inferred), to determine the missing transverse energy (which gives an estimate of the direction and energy of the neutrinos and other invisible particles), to reconstruct and identify taus from their decay products and more.

Clustering, blocking and linking

To dig deeper into the logic of the main tasks needed in a PF algorithm for reconstruction, please read these slides from one of our CMS colleages. Answer the questions below in our assignment form.

Question #5: What does the energy of a clustering seed in the calorimeters need to be?

Question #6: What are blocks?

Particles and corrections

After reading about forming blocks, you’re probably getting an idea of how the final particle identifications might be made from all the elements of a block.

The image below shows a cartoon of several possible blocks. ECAL clusters are shown in red and HCAL clusters are shown in blue. Inner tracks are drawn as black curves and muon chambers tracks (also called standalone muons) are depicted in green. The first physics objects that are built from blocks of clusters and tracks are global muons.

Note that the global muon on the left might not be isolated (there is a lot of track activity around), while the one on the right does seem to be an isolated one (of course, muons can also leave energy in the calorimeters). But the best particle hypothesis where muon tracks appear is “muon”! Any clusters or tracks linked to these standalone muons can now be removed from their respective blocks as global muon objects.

The remaining block elements are sorted into particle hypotheses in the following order:

So each cluster and track in each block is sorted into a single particle hypothesis. At each step, decisions have to be made about how to assign the new particle an energy and direction. CMS generally relies on inner tracks for momentum and direction measurements for the charged particles, and relies on calorimeter clusters for the energies of electrons, photons, and hadrons. These cluster energies need calibration, like this example for ECAL clusters:

Some particle hypothesis directly connect to physics objects: muons, electrons, and photons. Others are built from combining PF particles (often called “candidates”) into larger structures. For example, jet clustering algorithms can be used to form jets from showers of non-isolated electrons, muons, photons, and hadrons. In the first cartoon image of PF blocks, the activity on the left could be identified as a jet. Most algorithms in CMS use PF candidates.

As it was mentioned in the beginning, a list of physics object can be found in this guide from the CODP. There, one can also find extended references.

Key Points

  • The PF algorithm aims at reconstructing and identifying all stable particles in the event, which can be later used to build other physics objects.