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arXiv 1104.2404hep-phhep-exphysics.data-an
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高能对撞机数据的自动异常检测

Automatic anomaly detection in high energy collider data

Simon de Visscher, Michel Herquet

摘要:

我们解决的问题,自动异常检测高能对撞机数据。我们的方法是基于随机生成的运动学变量的解析表达式,然后可以进化的遗传编程程序,以提高他们的鉴别力。我们将这种方法应用到三个具体的情况下,以证明其可能的有用性,无论是作为一个详细的检查参考蒙特卡罗模拟和作为一个独立的工具,用于检测新物理签名的模型。

英文摘要:

We address the problem of automatic anomaly detection in high energy collider data. Our approach is based on the random generation of analytic expressions for kinematical variables, which can then be evolved following a genetic programming procedure to enhance their discriminating power. We apply this approach to three concrete scenarios to demonstrate its possible usefulness, both as a detailed check of reference Monte-Carlo simulations and as a model independent tool for the detection of New Physics signatures.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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