AI 中文总结
该研究将散射振幅视为程序，连接揭示解析结构与构建数值评估器的目标。通过自演化程序搜索进行缩放、结构和精确操作搜索，优化振幅计算，在多个过程测试中取得加速等成果，初步证明部分振幅优化可系统化。
AI 中文摘要
通过将散射振幅视为计算机程序，我们连接了两个目标：揭示有用的解析结构并为对撞机现象学构建高效的数值评估器。等效程序在多重性缩放、算术复杂性、抵消和运行时方面可能有很大差异。因此，振幅计算在解析表示、递归算法、色和螺旋度组织以及中间对象的重用方面定义了一个结构化搜索问题。我们将存储库规模的编码代理嵌入到具有固定评估器和目标的外部生成-评估-选择循环中，并研究三个优化目标。缩放搜索从BCFW递归转移到专门的固定-k分裂螺旋度转移算法，在评分网格上实现了805倍的几何平均加速。结构搜索将NMHV项重新组织成R不变风格的单元和胶合超级单元，减少项间抵消。在二十个QCD和电弱过程中，精确操作搜索将计数算术减少了47.7倍，并使事后Python运行时提高了5.9倍。在从n = 4到n = 6的匹配纯胶子组件测试中，演化引擎相对于测试的Sherpa-Comix精确和调用的速度优势从10倍增长到277倍，而其与在-O3编译的过程专用MadGraph5_aMC@NLO Fortran的差距从77.7倍缩小到13.7倍。这些搜索在数学表示之间移动，并将递归、对称性、基约简、动态规划和共享计算组合成混合振幅程序。它们提供了初步证据，表明振幅优化的部分内容可以通过自演化程序搜索系统化，同时将原生生成器集成和端到端事件吞吐量留作未来测试。生成器比较是一个孤立的精确矩阵元素调用，而不是对MadGraph或Sherpa的修改。
英文摘要
By viewing scattering amplitudes as computer programs, we connect two goals: exposing useful analytic structure and constructing efficient numerical evaluators for collider phenomenology. Equivalent programs can differ sharply in multiplicity scaling, arithmetic complexity, cancellation, and runtime. Amplitude calculation therefore defines a structured search problem over analytic representations, recursive algorithms, colour and helicity organisation, and reuse of intermediate objects. We embed repository-scale coding agents inside an external generate-evaluate-select loop with frozen evaluators and objectives, and study three optimisation targets. A scaling search moves from BCFW recursion to a specialised fixed-k split-helicity transfer algorithm, reaching an 805x geometric-mean speed-up on the scored grid. A structural search reorganises NMHV terms into R-invariant-style cells and glued supercells, reducing inter-term cancellation. Across twenty QCD and electroweak processes, an exact-operation search reduces counted arithmetic by 47.7x and gives a 5.9x post-hoc Python runtime improvement. In matched pure-gluon component tests from n=4 to n=6, the evolved engine's speed advantage over the tested Sherpa-Comix exact-sum call grows from 10x to 277x, while its gap to process-specialised MadGraph5_aMC@NLO Fortran compiled at -O3 narrows from 77.7x to 13.7x. The searches move between mathematical representations and combine recursion, symmetry, basis reduction, dynamic programming, and shared computation into hybrid amplitude programs. They provide initial evidence that parts of amplitude optimisation can be made systematic through self-evolving program search, while leaving native generator integration and end-to-end event throughput as future tests. The generator comparison is an isolated exact matrix-element call, not a modification of MadGraph or Sherpa.
Comments29 pages, 8 figures
