使用约束瓶颈自动编码器的数据驱动探测器信号表征
Data-driven detector signal characterization with constrained bottleneck autoencoders
摘要：
高能物理学中的一种常见技术是通过调谐到数据的模型来表征探测器的响应，所述模型建立从系统的物理参数到探测器的预期信号的参数映射。当基础模型未知时，很难应用这种方法，并且通常会进行简化假设，从而引入建模误差。在本文中，我们使用波形玩具模型来展示如何使用约束瓶颈自动编码器形式的深度学习来直接从数据中学习底层未知的检测器响应模型。结果表明，即使信号受到随机噪声的显著影响，也可以实现优异的性能结果。训练后的算法可以同时用于对模型的物理参数进行估计，以高保真度模拟检测器响应并对检测器信号进行降噪。
英文摘要：
A common technique in high energy physics is to characterize the response of a detector by means of models tunned to data which build parametric maps from the physical parameters of the system to the expected signal of the detector. When the underlying model is unknown it is difficult to apply this method, and often, simplifying assumptions are made introducing modeling errors. In this article, using a waveform toy model we present how deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The results show that excellent performance results can be achieved even when the signals are significantly affected by random noise. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.
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。
