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Automation of next-to-leading-order predictions for physics beyond the Standard Model at the LHC

超越大型强子对撞机标准模型的物理次领先阶预测的自动化

基本信息

批准号：
337250025
负责人：
Professor Dr. Stefan Dittmaier
金额：
--
依托单位：
Physikalisches Institut
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/337250025?language=en
关键词：
Automation next leading order predictions

项目摘要

In the second phase of data taking at the Large Hadron Collider (LHC), precision will be of crucial importance, both in the search for new physics and in the the ultimate test of the successfully working Standard Model (SM). Precise theoretical predictions for particle reactions have to include quantum corrections of the strong and electroweak interactions, at least to next-to-leading order (NLO). The complexity of this problem calls for automation on the computational side. Recent years have seen tremendous progress in this direction for predictions within the SM - a process that was pushed by several groups using different concepts. In this project, we build on the well established programs OpenLoops and Collier, which were developed by our groups for automated amplitude generation and their numerical evaluation, and strive for the extension of OpenLoops to physics beyond the SM (BSM). Aiming at a rather flexible approach in general, we start with BSM models with extended scalar sectors and widely model-independent effective theories with higher-dimensional operators. First phenomenological applications will cover issues in electroweak precision physics with Higgs and electroweak vector bosons in those BSM models. The combination of OpenLoops+Collier with the multi-purpose event generator Sherpa offers a straightforward parton-shower matching and multi-jet merging framework and thus embeds our result into a toolchain that can be directly used by experimentalists in data analyses.

在大型强子对撞机（LHC）数据采集的第二阶段，无论是在寻找新物理学还是在成功运行的标准模型（SM）的最终测试中，精度都将至关重要。粒子反应的精确理论预测必须包括强和电弱相互作用的量子修正，至少要达到次一级（NLO）。这个问题的复杂性要求在计算方面实现自动化。近年来，SM内部的预测在这个方向上取得了巨大的进展——这一过程是由几个使用不同概念的小组推动的。在本项目中，我们以我们的团队开发的用于自动振幅生成及其数值评估的成熟程序OpenLoops和Collier为基础，努力将OpenLoops扩展到SM （BSM）以外的物理领域。针对一般情况下比较灵活的方法，我们从具有扩展标量扇区的BSM模型和具有高维算子的广泛模型无关的有效理论开始。首先，现象学应用将涵盖在这些BSM模型中的希格斯和电弱矢量玻色子的电弱精密物理问题。OpenLoops+Collier与多用途事件生成器Sherpa的结合提供了一个简单的部分-阵雨匹配和多射流合并框架，从而将我们的结果嵌入到一个工具链中，可以直接用于实验人员的数据分析。