基于神经网络的一维干涉互相关迹线脉冲重建
Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces
AI总结：
针对一维脉冲重建受代数基本定理限制而难以无歧义求解、迭代算法通用性不足的问题，提出用深度神经网络求解带约束的干涉互相关迹线重建，实现快速可靠的完整脉冲表征。
摘要：
任何超快光谱实验通常都伴随着超短脉冲表征这一必要流程。大多数脉冲表征方法求解的是一维（例如通过干涉测量）或二维（例如通过频率分辨测量）问题。由于二维脉冲重建问题具有超定性质，其求解通常更为一致。相比之下，受代数基本定理的根本限制，一维脉冲重建问题不可能无歧义地求解。在涉及额外约束的情况下，一维问题或许可以求解，但现有迭代算法缺乏通用性，且对于复杂脉冲形状常陷入停滞。本文采用深度神经网络无歧义地求解带约束的一维脉冲重建问题，并展示了利用干涉互相关时间迹线（由具有部分光谱重叠的脉冲决定）实现快速、可靠且完整脉冲表征的潜力。
英文摘要：
Any ultrafast optical spectroscopy experiment is usually accompanied by the necessary routine of ultrashort-pulse characterisation. The majority of pulse characterisation approaches solve either a one-dimensional (e.g. via interferometry) or a two-dimensional (e.g. via frequency-resolved measurements) problem. Solution of the two-dimensional pulse-retrieval problem is generally more consistent due to problem's over-determined nature. In contrast, the one-dimensional pulse-retrieval problem is impossible to solve unambiguously as ultimately imposed by the fundamental theorem of algebra. In cases where additional constraints are involved, the one-dimensional problem may be possible to solve, however, existing iterative algorithms lack generality, and often stagnate for complicated pulse shapes. Here we use a deep neural network to unambiguously solve a constrained one-dimensional pulse-retrieval problem and show the potential of fast, reliable, and complete pulse characterisation using interferometric cross-correlation time traces (determined by the pulses with partial spectral overlap).
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。
