论文标题
机器学习辅助3D位置重建大型Lacl $ _ {3} $ Crystals
Machine Learning aided 3D-position reconstruction in large LaCl$_{3}$ crystals
论文作者
论文摘要
我们研究了五个不同的模型，以重建五个大型\ lacls单片晶体的3D $γ$ ray命中坐标，并在光学上与像素化的硅光层流相结合。这些闪光灯的基本表面为50 $ \ times $ 50毫米$^2 $，五个不同的厚度，从10毫米到30毫米。其中四个模型是分析处方，一个是基于卷积神经网络。使用分析模型，在横向晶体平面上获得了接近1-2mm FWHM的平均分辨率，以在10 mm至20 mm之间的晶体厚度。对于较厚的晶体，获得了约3-5毫米FWHM的平均分辨率。相互作用分辨率的深度在1mm和4 mm之间，具体取决于相互作用点与光电传感器表面的距离。我们提出了一种机器学习算法，以纠正线性扭曲和销钉效果。后者允许人们保持大约70-80 \％晶体表面的较大视野，无论晶体厚度如何。这项工作旨在通过Compton成像能力（I-TED）优化所谓的总能量检测器的性能，以进行飞行时间中子中子捕获横截面测量。
We investigate five different models to reconstruct the 3D $γ$-ray hit coordinates in five large \lacls monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 $\times$ 50 mm$^2$ and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5~mm fwhm are obtained. Depth of interaction resolutions between 1mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70-80\% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.
