Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
---|---|---|
Article Number | 03062 | |
Number of page(s) | 12 | |
Section | Offline Computing | |
DOI | https://doi.org/10.1051/epjconf/202125103062 | |
Published online | 23 August 2021 |
https://doi.org/10.1051/epjconf/202125103062
Recent advances in ADL, CutLang and adl2tnm
1 Department of Physics, Florida State University, Tallahassee, FL, USA
2 Center for High Energy Physics, Kyungpook National University, Daegu, South Korea
3 Physics and Astronomy Department, University of California at Irvine, Irvine, CA, USA
4 The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
* e-mail: harry@hep.fsu.edu
** e-mail: ssekmen@cern.ch
*** e-mail: gokhan.unel@cern.ch
**** e-mail: apaul@ictp.it
Published online: 23 August 2021
This paper presents an overview and features of an Analysis Description Language (ADL) designed for HEP data analysis. ADL is a domainspecific, declarative language that describes the physics content of an analysis in a standard and unambiguous way, independent of any computing frameworks. It also describes infrastructures that render ADL executable, namely CutLang, a direct runtime interpreter (originally also a language), and adl2tnm, a transpiler converting ADL into C++ code. In ADL, analyses are described in humanreadable plain text files, clearly 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. Recent studies demonstrate that adapting the ADL approach has numerous benefits for the experimental and phenomenological HEP communities. These include facilitating the abstraction, design, optimization, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents and long term preservation of the analyses beyond the lifetimes of experiments. Here we also discuss some of the current ADL applications in physics studies and future prospects based on static analysis and differentiable programming.
© The Authors, published by EDP Sciences, 2021
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