# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 1 Introduction  
Whistlers, as an invaluable natural resource for diagnosing the entire circumterrestrial space, have played a significant role in upper atmospheric and space physics since the pioneering works by [PERSON] (1953) [1], [PERSON] et al. (1956) [2], and [PERSON] et al. (1960) [3]. Primarily employed to determine plasma density [4, 5, 6], whistlers have proven to be versatile tools for exploring the Earth's magnetosphere and ionosphere. Notable applications include measuring ionosphere-plasmasphere coupling [7], the discovery of the plasmapause [8], and the development of an empirical model of equatorial electron density in the magnetosphere [9]. Recent studies have also suggested that whistler waves can efficiently accelerate electrons in the outer radiation belt [10, 11], potentially leading to electron precipitation into the upper atmosphere [12, 13]. Despite decades of investigations, whistler-mode wave particle interactions remain an active area of research, and the importance of whistlers on radiation belt electron losses is still debated [14, 15]. Moreover, their effects on the terrestrial electromagnetic environment are not entirely understood, with several unresolved questions remaining [16]. Large-scaledata analysis of whistlers represents the most promising approach to address these open scientific problems. Given the variability of the geomagnetic environment, collecting a statistically significant number of \"clean\" events can take weeks or even years [17; 18; 19]. As manual identification of such events is a time-consuming and inefficient process, automatic detection tools are an essential requirement for this type of analysis [20]. The major challenge in implementing automatic signal detection methods lies in distinguishing the signal of interest from background noise [19; 20]. Furthermore, the non-stationary nature of whistler waves, characterized by rapidly varying frequencies, poses a significant challenge to accurate feature extraction using conventional spectral analysis techniques [21]. Several whistler detection techniques leverage their falling tone frequency pattern in a time-frequency spectrum (see e.g., Figure 1 in [22], Figure 1 in [23] or Figure 2a in [24], reporting typical Very-Low-Frequency (VLF) records of a whistler in time-frequency spectra). The Automatic Whistler Detector (AWD) method [22] pioneered this type of detection technique. This method is based on image correlation between a time-frequency spectrum obtained using Short Time Fourier Transforms (STFTs) and a model whistler (the model whistler is generated by the Bernard approximation [25]). Following this approach, several recognition methods have been proposed (e.g., [26; 27; 28]), employing different image processing and noise removal techniques in the attempt to improve the effectiveness of the method. While these methods have demonstrated significant value, they are not without limitations. Specifically, they are susceptible to image noise and exhibit reduced accuracy in noisy environmental conditions [18]. In recent years, deep learning methods based on Convolutional Neural Networks (CNNs) have also been explored [20; 29]. However, while offering potential for automated whistler detection, the deep architectures employed by these methods preclude a clear understanding of the specific criteria and decision-making processes employed in whistler identification. Furthermore, such methods require substantial computational resources and time (e.g., in [30], the authors employed a system with an NVIDIA Corporation GV100 GL GPU with a memory size of 32 GB; in [20], the authors utilized an NVIDIA TITAN V GPU and required 3.2 h of training for a dataset of 1038 spectrograms). In this study, we present the newly developed WhISPER algorithm for whistler detection, which bypasses time-frequency spectra image generation and analysis, thereby eliminating the requirement for GPU computations and resulting in exceptional computational efficiency.