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arXiv 2004.10744hep-ex
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用于识别高粒度量热计中长寿命粒子的快速卷积神经网络

Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter

Juliette Alimena, Yutaro Iiyama, Jan Kieseler

摘要:

我们提出了第一个概念证明,直接使用基于神经网络的模式识别来触发来自位移粒子的不同量热计签名,例如那些来自外来长寿粒子衰变的签名。该研究是为一个高粒度前向量热计类似的计划高粒度量热计的CMS探测器在欧洲核子研究中心大型强子对撞机的高亮度升级。在没有假设预测长寿命粒子的特定模型的情况下,我们证明了一个简单的卷积神经网络,原则上可以部署在专用的快速硬件上,可以有效地识别从位移粒子到低能量的簇射,同时提供低触发率。

英文摘要:

We present a first proof of concept to directly use neural network based pattern recognition to trigger on distinct calorimeter signatures from displaced particles, such as those that arise from the decays of exotic long-lived particles. The study is performed for a high granularity forward calorimeter similar to the planned high granularity calorimeter for the high luminosity upgrade of the CMS detector at the CERN Large Hadron Collider. Without assuming a particular model that predicts long-lived particles, we show that a simple convolutional neural network, that could in principle be deployed on dedicated fast hardware, can efficiently identify showers from displaced particles down to low energies while providing a low trigger rate.

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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