论文标题
在电阻硅探测器中，机器学习算法的首次应用到位置重建
First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors
论文作者
论文摘要
RSD（电阻AC耦合硅探测器）是基于LGAD（低增益雪崩二极管）技术的N-P硅传感器，其具有在整个传感器区域的连续增益层。这些传感器的真正创新特征是，在几个像素上可以看到由电离粒子诱导的信号，从而允许使用重建技术，这些技术结合了许多读出通道的信息。在此贡献中，提出了机器学习技术在RSD设备上的首次应用。将该技术的空间分辨率与使用信号共享机制的分析描述的标准RSD重建方法进行了比较。通过模拟和真实数据组合训练的多输出回归算法，可为具有100 $ $ $ $像素的传感器的传感器提供小于2美元的空间分辨率。还讨论了未来改进的前景。
RSDs (Resistive AC-Coupled Silicon Detectors) are n-in-p silicon sensors based on the LGAD (Low-Gain Avalanche Diode) technology, featuring a continuous gain layer over the whole sensor area. The truly innovative feature of these sensors is that the signal induced by an ionising particle is seen on several pixels, allowing the use of reconstruction techniques that combine the information from many read-out channels. In this contribution, the first application of a machine learning technique to RSD devices is presented. The spatial resolution of this technique is compared to that obtained with the standard RSD reconstruction methods that use analytical descriptions of the signal sharing mechanism. A Multi-Output regressor algorithm, trained with a combination of simulated and real data, leads to a spatial resolution of less than 2 $μm$ for a sensor with a 100 $μm$ pixel. The prospects of future improvements are also discussed.
