Welcome and orientation

Live Presentation: Workshop Introduction

• Kati Lassila-Perini (Helsinki Institute of Physics (FI))

Live Hands-on lesson: Dataset scouting

• Matthew Bellis (Cornell University/Siena College (US))

This lesson is designed to teach you how to use the command-line to explore the directories where the data is stored. In this way, you can see what triggers were applied when the data was taken and what Monte Carlo samples are available for the run period you are interested in. You’ll also be shown how to do a first-order inspection of some of these datafiles, just to see what is stored in them.

Live Hands-on lesson: Trigger manipulation

• Edgar Fernando Carrera Jarrin (Universidad San Francisco de Quito (EC))

In this lesson you will: learn what the CMS trigger system is learn how to select and understand triggers for you analysis learn how to obtain trigger prescales and acceptance bits

Async Demo: Physics objects

• Julie Hogan (Brown University, Bethel University (US))

In this video tutorial we will introduce you to CMS particle flow and physics objects. Later, in the afternoon, you will dive into the code. Enjoy! A youtube video link is attached as well as the slides presented in it.

Async Hands-on lesson: Physics objects I

• Julie Hogan (Brown University, Bethel University (US))

When a physicist approaches an analysis using CMS data, they typically rely on the reconstruction algorithms developed by CMS to interpret detector signals into meaningful physics objects. In code, the result of these recontruction algorithms takes the form of several C++ classes that will be introduced briefly in this lesson. The content of the C++ class reflects the nature of the physics object it represents. In this lesson we will study several fundamental particles: muons, electrons, photons, and tau leptons. The first three particles are special in CMS, because they are reconstructed as single “particle-flow candidates”. The Particle Flow algorithm (CITE ME) combines detector signals from multiple CMS subdetector systems to categorize all energy deposits as muons, electrons, photons, neutral hadrons, or charged hadrons. Tau leptons are more complex because they are not stable and have several detector signatures that include muons, electrons, photons, and/or hadrons. In the next lesson we will approach even more complex objects such as jet and missing transverse energy. After exploring the code elements that are common to all CMS physics objects we will look at muons, electrons, photons, and tau leptons in more detail to understand the options for identifying these particles in your analysis. The final episode (MAYBE IN SELECTION LESSON?) will show how an analyzer can combine different identification elements into selection criteria.

The Future is Open: Adventures with Public Collider Data

• Jesse Thaler (MIT)

Fermilab employees and users can access the Zoom link below (Services login required): https://fermipoint.fnal.gov/org/ood/LabLeadership/Shared%20Documents/Zoom%20link%20for%20colloquium.docx?d=wddecabdd5efe44ee91ba775647366a0a&csf=1&e=XzG3Ib Please note: you will need the passcode to enter the zoom Anyone else can obtain the Zoom link the day of the colloquium by emailing Barb Kronkow at kronkow@fnal.gov In November 2014, the CMS experiment at the Large Hadron Collider made the unprecedented move of releasing research-grade particle physics data for unrestricted use. I am a theoretical particle physicist, and for the first time, I had access to real collision data from a cutting-edge experiment, as well as an opportunity to demonstrate the scientific value of public data access. Over the past six years, my research group has carried out a number of innovative analyses using the CMS Open Data. In this colloquium, I highlight some of our research successes as well as some of the challenges we faced using public collider data to explore physics in and beyond the Standard Model.

Live Hands-on lesson: Physics objects II - Jets and MET

• Julie Hogan (Brown University, Bethel University (US))
• Farrah Simpson (Brown University (US))
• Nikolas Pervan (Brown University (US))

Jets and missing transverse energy (MET) are critical for CMS physics analyses. They are more complex than most of the objects we discussed in the previous lesson, because they are reconstructed using multiple particle-flow candidates. After all candidates have been built from the tracks and energy deposits in CMS, they can be “clustered” using a variety of algorithms into composite objects called “jets”. Missing transverse energy clusters, in a sense, all candidates in the entire detector: it is the negative vector sum of the momentum of all candidates. In this lesson we will explore the basic utilities for jets and MET, how to identify jets that arise from interesting original particles such as bottom quarks, and possibly how to correct jets and MET for differences between data and simulation.

Live Hands-on lesson: Pre-selection and skimming

• Farrah Simpson (Brown University (US))
• Julie Hogan (Brown University, Bethel University (US))
• Nikolas Pervan (Brown University (US))

In this lesson we will review the CMS data flow and summarize the selections that have been made up through the NanoAOD production. We will also go over how to produce your own set of NanoAOD files, though this will not be required to continue with the workshop.

Live Hands-on lesson: Object ID and selection.

• Julie Hogan (Brown University, Bethel University (US))
• Nikolas Pervan (Brown University (US))
• Farrah Simpson (Brown University (US))

With physics object prepared and NanoAOD files created, we are ready to begin thinking about an actual physics analysis! In the previous exercises, you learned how to access and store object information from an AOD file and convert the AOD file to NanoAOD. The Events tree within the NanoAOD files contains all the derived information required for many searches or measurements. We will study a search for the Higgs boson in the tau tau decay channel – you can go back to the pre-exercises to find the published paper.

Live Hands-on lesson: Plotting and interpretation

• Matthew Bellis (Cornell University/Siena College (US))

This exercise will walk you through the process of making some basic plots, once you have skimmed some data and produced ROOT files. We’ll make these plots using ROOT and calling ROOT from python using the PyROOT module. Note, that you could choose other approaches to making plots, such as using matplotlib, but the ROOT files need to be accessed with ROOT or uproot so we’ll stick with ROOT for now. Once we’ve made the plots, we’ll spend some time examining them and trying to interpret the distributions so that we can develop some intuition for how things look.

Async Demo: Luminosity and data quality.

• Thomas McCauley (University of Notre Dame (US))

Welcome. In this lesson you will: learn what luminosity is and why it’s important learn how luminosity is measured learn how to calculate luminosity

Async Hands-on lesson: Efficiency Studies using the Tag and Probe Method

• Thomas Gaehtgens
• Allan Da Silva Jales (Universidade do Estado do Rio de Janeiro (BR))

Live Demo: CMS analysis in the cloud

• Adelina Eleonora Lintuluoto (University of Helsinki (FI))
• Clemens Lange (CERN)

Live Hands-on lesson: CMS analysis in the cloud

• Clemens Lange (CERN)
• Adelina Eleonora Lintuluoto (University of Helsinki (FI))

Live Hands-on lesson: Background estimation - the ABCD method

• Santeri Laurila (CERN)

Live Featured Demo: ADL

• Sezen Sekmen (Kyungpook National University (KR))

Wrap up and time for discussion