Improving CutLang with New Features

This document explains the new features added to the analysis description language CutLang as a part of the Summer student project. This summer student project is supervised by Gökhan Ünel, Sezen Sekmen and David Francis. This project is about an Analysis Description Language (ADL), a domain specific computer language that aims to provide a clear, human readable way to define collider data analyses in high energy particle physics (HEP) along with an interpretation framework of that language. The ADLs are becoming popular as proven by a dedicated workshop organized Fermilab (https://indico.cern.ch/event/769263/). On one hand, LHADA (Les Houches Analysis Description Accord proposal), was designed by a group of LHC experimentalists and phenomenologists as a text-based language that can express analysis components for a wide range of physics analyses. The analyses are expressed as human readable text files including simple variable definitions, object selections, and event selections, supplemented by external functions for calculation of more complex analysis quantities. Several prototype interfaces are under development for analysis frameworks such as TheNtupleMaker, CheckMate and Rivet. On the other hand, a proof of principle (PoP) implementation of the interpreted ADL idea, CutLang, achieved using C++ as a layer over the CERN data analysis framework ROOT, is presently available. The ADL in CutLang is more than 90% similar with LHADA. In CutLang, HEP analyses can be written in a simple manner, as a set of commands in human readable text files, which are interpreted by the framework at runtime. The initial experience with CutLang has shown that a just-in-time interpretation of a human readable HEP specific language is a practical and efficient alternative to analysis writing using compiled languages such as C++. The interpreted approach is seen to speed up analysis design especially for beginners and phenomenologists. Moreover, as it is a declarative language, the user does not need to have coding skills. In addition, CutLang’s ability to process events from multiple data sources such as Delphes, ATLAS and CMS Open data could prove useful also to the CERN OpenData project. In this project, some new features and corresponding example analysis have been added to CutLang. [1], [2]