Jafari Rana, Khosravi Soroush D, Trebino Rick
School of Physics, Georgia Institute of Technology, 837 State Street NW, Atlanta, GA, 30332, USA.
Mathematics & Physics Department, Queens University of Charlotte, 1900 Selwyn Ave, Charlotte, NC, 28274, USA.
Sci Rep. 2022 Dec 5;12(1):21006. doi: 10.1038/s41598-022-25193-3.
We describe a reliable approach for determining the presence of pulse-shape instability in a train of ultrashort laser pulses. While frequency-resolved optical gating (FROG) has been shown to successfully perform this task by displaying a discrepancy between the measured and retrieved traces for unstable trains, it fails if its pulse-retrieval algorithm stagnates because algorithm stagnation and pulse-shape instability can be indistinguishable. So, a non-stagnating algorithm-even in the presence of instability-is required. The recently introduced Retrieved-Amplitude N-grid Algorithmic (RANA) approach has achieved extremely reliable (100%) pulse-retrieval in FROG for trains of stable pulse shapes, even in the presence of noise, and so is a promising candidate for an algorithm that can definitively distinguish stable and unstable pulse-shape trains. But it has not yet been considered for trains of pulses with pulse-shape instability. So, here, we investigate its performance for unstable trains of pulses with random pulse shapes. We consider trains of complex pulses measured by second-harmonic-generation FROG using the RANA approach and compare its performance to the well-known generalized-projections (GP) algorithm without the RANA enhancements. We show that the standard GP algorithm frequently fails to converge for such unstable pulse trains, yielding highly variable trace discrepancies. As a result, it is an unreliable indicator of instability. Using the RANA approach, on the other hand, we find zero stagnations, even for highly unstable pulse trains, and we conclude that FROG, coupled with the RANA approach, provides a highly reliable indicator of pulse-shape instability. It also provides a typical pulse length, spectral width, and time-bandwidth product, even in cases of instability.
我们描述了一种用于确定超短激光脉冲序列中脉冲形状不稳定性的可靠方法。虽然频率分辨光学门控（FROG）已被证明通过显示不稳定序列的测量迹线和检索迹线之间的差异来成功执行此任务，但如果其脉冲检索算法停滞，它就会失败，因为算法停滞和脉冲形状不稳定性可能无法区分。因此，需要一种即使在存在不稳定性的情况下也不会停滞的算法。最近引入的检索幅度N网格算法（RANA）方法在FROG中对于稳定脉冲形状的序列实现了极其可靠（100%）的脉冲检索，即使存在噪声，因此是一种有前途的算法候选者，可用于明确区分稳定和不稳定的脉冲形状序列。但它尚未被考虑用于具有脉冲形状不稳定性的脉冲序列。所以，在这里，我们研究它对于具有随机脉冲形状的不稳定脉冲序列的性能。我们考虑使用RANA方法通过二次谐波产生FROG测量的复脉冲序列，并将其性能与没有RANA增强功能的著名广义投影（GP）算法进行比较。我们表明，标准GP算法对于此类不稳定脉冲序列经常无法收敛，导致迹线差异高度可变。因此，它是不稳定性的不可靠指标。另一方面，使用RANA方法，我们发现即使对于高度不稳定的脉冲序列也没有停滞情况，并且我们得出结论，FROG与RANA方法相结合提供了脉冲形状不稳定性的高度可靠指标。即使在存在不稳定性的情况下，它还能提供典型的脉冲长度、光谱宽度和时间带宽积。
