HEP data-processing frameworks are essential ingredients in getting from raw data to physics results. But they are often tricky to use well, and they present a significant learning barrier for the beginning HEP physicist. In addition, existing frameworks typically support rigid, collider-based data models, which do not map well to neutrino-physics experiments like DUNE. Neutrino physicists thus expend significant effort working around framework limitations instead of using a framework that directly supports their needs. Presented here is Meld, a Fermilab R&D project, which intends to address these limitations. By leveraging modern C++ capabilities, state-of-the-art concurrency libraries, and a flexible data model, it is possible for beginning (and seasoned) HEP physicists to execute framework programs easily and efficiently, with minimal coupling to framework-specific constructs. Meld aims to directly support the frameworks needs of neutrino experiments like DUNE as well as the more common collider-based experiments.

翻译：高能物理数据处理框架是将原始数据转化为物理结果的关键组成部分，但往往难以有效运用，并对初入高能物理领域的研究者构成显著的学习障碍。此外，现有框架通常支持僵化的对撞机数据模型，难以适配如DUNE等中微子物理实验的需求。因此，中微子物理学家需要花费大量精力克服框架限制，而非使用直接满足其需求的框架。本文介绍的是Fermilab的研发项目Meld，旨在解决上述局限性。通过利用现代C++能力、先进并发库和灵活的数据模型，初入（及资深）高能物理学家能够轻松高效地执行框架程序，同时最大程度减少与框架特定结构的耦合。Meld旨在直接支持如DUNE等中微子实验以及更常见的对撞机实验的框架需求。