Reinterpretation with Open Data

This lesson aims to explore reinterpretation and role of Open Data in it. It introduces reinterpretation concepts and provides a hands-on exercise that reinterprets a familiar standard model analysis in terms of a new physics signal. The exercises are implemented using Analysis Description Language and CutLang, a construct designed to express the physics content of an analysis a transparant and organized manner and runtime interpret the description.

Prerequisites

Please make sure that you have followed the Docker pre-exercises and are familiar with Docker containers.

Schedule

	Setup	Download files required for the lesson
00:00	1. What is reinterpretation and what is the role of open data?	What is reinterpretation? What are exact and optimized reinterpretation? What inputs are needed to perform reinterpretation? What is the role of open data in reinterpretation?
00:20	2. Introducing ADL and CutLang	What is ADL? What is CutLang? Why we use ADL/CutLang in repinterpretation studies?
00:35	3. Installing CutLang	How do I access information about CutLang in general? How do I install CutLang via Docker? How do I test my installation? Can I already run some ADL examples with CutLang?
01:15	4. Open data reinterpretation with ADL/CutLang: ttbar to vector-like T quark	How can we do exact reinterpretation with open data and ADL/CutLang? How can we do optimized reinterpretation with open data and ADL/CutLang? How can we add analysis definitions to the ADL file? How can we optimize an analysis, find discriminating variables? What information we help us assess the adequacy of the analysis?
02:05	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.