使用深度学习模型的束流线导引
Beamline Steering Using Deep Learning Models
AI总结：
针对粒子加速器束流导引耗时费力的问题，研究开发了多种前馈神经网络，发现小规模模型（33输入13输出）优于大规模模型（73输入50输出），并分析了其性能不及SVD的原因。
摘要：
束流导引涉及对粒子加速器电子束相对于准直器旋转轴入射到X射线靶上的角度和位置的校准。束流导引是光源的一项基本任务。由于加速器每次使用时磁铁都需要重新校准，Linac To Undulator的导引和瞄准非常困难。然而，在束流线的每次使用中，其当前的导引方法在校准角度和位置时会遇到问题。操作员在此任务上花费了大量时间和资源。我们开发了多个具有不同超参数、输入和输出的前馈神经网络，以比较它们的性能。具体而言，我们具有33个输入和13个输出的较小模型优于具有73个输入和50个输出的较大模型。我们对较大模型性能不足提出以下解释。首先，训练时间和计算能力的不足限制了模型成熟的能力。给予更多时间，我们的模型将优于SVD。其次，当模型输入规模增加时，噪声也会增加。在这种情况下，更多的输入对应于LINAC加速器上更长的距离。寻求做出更多预测的针对性较弱且规模较大的模型本质上会比SVD表现更差。
英文摘要：
Beam steering involves the calibration of the angle and position at which a particle accelerator's electron beam is incident upon the x-ray target with respect to the rotation axis of the collimator. Beam Steering is an essential task for light sources. The Linac To Undulator is very difficult to steer and aim due to the changes of each use of the accelerator there must be re-calibration of magnets. However with each use of the Beamline its current method of steering runs into issues when faced with calibrating angles and positions. Human operators spend a substantial amount of time and resources on the task. We developed multiple different feed-forward-neural networks with varying hyper-parameters, inputs, and outputs, seeking to compare their performance. Specifically, our smaller models with 33 inputs and 13 outputs outperformed the larger models with 73 inputs and 50 outputs. We propose the following explanations for this lack of performance in larger models. First, a lack of training time and computational power limited the ability of our models to mature. Given more time, our models would outperform SVD. Second, when the input size of the model increases the noise increases as well. In this case more inputs corresponded to a greater length upon the LINAC accelerator. Less specific and larger models that seek to make more predictions will inherently perform worse than SVD.
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。
