CutLang V2: towards a unified Analysis Description Language

TL;DR

CutLang V2 introduces an improved, unified analysis description language for collider data, enabling clearer, framework-independent analysis descriptions that facilitate preservation, validation, and communication within high-energy physics research.

Contribution

The paper presents the latest version of CutLang, a runtime interpreter for ADL, with syntax enhancements, new features, and real-world LHC analysis examples, advancing analysis standardization.

Findings

Enhanced syntax for better ADL compatibility

Added features like object combinatorics, tables, and event saving

Demonstrated real-life LHC analysis applications

Abstract

We will present the latest developments in CutLang, the runtime interpreter of a recently-developed analysis description language (ADL) for collider data analysis. ADL is a domain-specific, declarative language that describes the contents of an analysis in a standard and unambiguous way, independent of any computing framework. In ADL, analyses are written in human-readable plain text files, separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Adopting ADLs would bring numerous benefits to the LHC experimental and phenomenological communities, ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Since their initial release, ADL and CutLang have been used for implementing and running numerous LHC analyses. In this process, the original syntax from CutLang v1 has been modified for better ADL compatibility, and the interpreter has been adapted to work with that syntax, resulting in the current release v2. Furthermore, CutLang has been enhanced to handle object combinatorics, to include tables and weights, to save events at any analysis stage, to benefit from multi-core/multi-CPU hardware among other improvements. In this contribution, these and other enhancements are discussed in details. In addition, real life examples from LHC analyses are presented together with a user manual.