## 1 Introduction  
New developments in solid-state lasers have led to the availability of higher-power laser beam sources at relatively low investment costs [1]. This extra power is often used to increase the welding speed, resulting in a significant improvement in economic efficiency. However, while increasing the welding speed, the demands on process monitoring methods increase as well. In this context, welding of high-alloy steels is particularly challenging because the formation of spatter increases significantly as the welding speed increases [2, 3], especially between welding speeds of 10 m/min and 16 m/min, and the high thermal expansion coefficient and low thermal conductivity contribute to the formation of distortion [4, 5]. This is particularly critical in butt weld applications, where the thermal expansion during the melting process and the thermal contraction during the solidification of the material create a gap between the parts to be joined. Since the gap has a significant impact on process stability and weld quality [6], gap monitoring during the welding process is essential for quality assurance and scrap reduction. Recent results have shown that a critical gap can be formed in less than 1 s for welding at a welding speed of 1 m/min [7], and an increase in welding speed results in a reduction in critical gap size [8], so a gap monitoring system for laser butt welding requires high temporal resolution and low latency. There exist various in-process monitoring methods to detect joint gaps for butt weld applications, such as optical and tactile methods. However, these methods are cost-intensive as well as difficult to install, necessitating the usage of practice-friendly methods. Due to its low cost and installation flexibility, acoustic process monitoring can be beneficial and has been proven to provide weld-relevant information such as penetration depth [9; 10] or heat conduction level [11; 12]. For tackling the aforementioned challenge in high-speed welding of high-alloy steels, acoustic process monitoring has also been demonstrated to successfully detect the spatter formation [13] and quantify the gap size in butt weld applications [14]. When it comes to real-time applications, however, it has remained unanswered whether acoustic process monitoring is indeed feasible in real-time or not. Among the existing studies, the reason for selecting signal duration is not clearly stated, raising the question of whether the signals are sufficiently long or can be possibly shortened. In short, the temporal resolution of acoustic process emissions for providing weld-relevant information has not yet been addressed. Undoubtedly, the temporal resolution varies depending on the type of information. Hence, this work focuses on the application of gap detection in laser beam butt welding configuration, aiming to answer this question. Upon investigating its temporal resolution, however, the informativeness of signal segments should be evaluated and desirably quantified. Since signal informativeness has not been the focus of existing studies, no metric has been yet established. Nevertheless, existing studies provide insights on selecting criteria for correlating acoustic emissions with the welding process and/or weld-relevant parameters. A common approach is based on signal energy, using raw signals [11] or denoised ones via simple filtering [9; 15; 16] or wavelet [17]. There exist statistical approaches as well by combining different statistical measures [17; 18] or by computing the Renyi entropy of high-resolution time-frequency distribution [19]. Yet, as the authors of [17; 18; 19] suggest, relying on statistical measures alone is not sufficient for classifying different weld characteristics. On the other hand, the emerging number of machine learning approaches [20; 21; 22] utilize signal structure, yielding higher classification accuracy than solely based on signal energy or statistical measures.