# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods
### Selection Thresholds  
To validate the identification of IPSD peaks as a method for whistler detection within the top-side ionosphere, we performed a detailed visual analysis of 1346 spectrograms, generated using the methodology detailed in the preceding section. The validation process was conducted on 1346 local maxima identified within the IPSD\({}_{\text{t}}\) of SCM-ELF data. These maxima were selected from randomly chosen diurnal and nocturnal semiorbits, spanning various years, seasons, and geomagnetic conditions, to rigorously evaluate the efficacy of WhISPER across diverse scenarios. For each identified local maximum, the following quantities were computed:  
* peak amplitude: IPSD\({}_{\text{t}}\) [nT\({}^{2}\)];
* peak prominence: \(Pr\), which is a measure of how much the peak stands out relative to other peaks [nT\({}^{2}\)];
* peak prominence-to-amplitude ratio: \(R\) [dimensionless];
* peak width: \(W\), expressed as a multiple of the temporal window (TW) employed for SCM PSD calculations (400 ms). The selection is applied to the total IPSD in order to take into account enhancements in a single or multiple component(s). To filter out signals with characteristics inconsistent with whistler waves, we applied the following selection criteria to these quantities:  
1. IPSD\({}_{\text{t}}\) > \(T_{1}\);
2. \(Pr>T_{2}\);
3. \(R=\frac{Pr}{\text{IPSD}_{\text{t}}}>T_{3}\);
4. \(W<T_{4}\cdot\text{TW}\). Condition 1 imposes a threshold, \(T_{1}\), on IPSD\({}_{\text{t}}\), selecting only local maxima whose peak amplitude exceeds this threshold. This condition excludes low-energy peaks, filtering  
Figure 7: Electric field waveforms (left panels) and spectrograms (right panels) within a \(\simeq 2\) s window centered on \(t_{b}\) (red dashed line, corresponding to the minor representative peak in Figure 2). The top to bottom panels correspond to the X, Y, and Z components, respectively. out very faint signals. Conditions 2 and 3 impose thresholds \(T_{2}\) and \(T_{3}\), respectively on \(Pr\) and \(R\), filtering out peaks located within regions of broad spectral enhancement, which are inconsistent with typical whistler wave characteristics. Condition 4 restricts peak width, retaining only local maxima with extents consistent with the typical temporal duration of whistlers. To determine the optimal threshold parameters (\(T_{1}\), \(T_{2}\), \(T_{3}\), \(T_{4}\)) that accurately select all and only peaks associated with whistler waves, three distinct parameter sets were evaluated, utilizing visual inspection of spectrograms from the subset of 1346 randomly chosen local maxima. The first parameter set was derived from an analysis of the peak identified at \(t_{b}\) (red dashed line in Figure 2), which was characterized by \(\text{IPSD}_{\text{I}}=0.31\) nT\({}^{2}\), \(Pr=0.28\) nT\({}^{2}\), \(R=0.87\), and \(W=1.21\) TW. As illustrated in Figure 4, a clear whistler wave is associated with this peak, despite its low amplitude waveform (<0.1 nT) and increased spectrogram noise (SNR is reduce by a factor\(\sim\)10) relative to the largest IPSD peak (shown in Figure 3). Consequently, the first parameter set was configured to identify events similar to that shown in Figure 4, specifically \(T_{1}=0.28\) nT\({}^{2}\), \(T_{2}=0.25\) nT\({}^{2}\), \(T_{3}=0.5\), and \(T_{4}=2\). The second and third parameter sets were chosen to provide varying degrees of selection stringency. Specifically, the second set applies more stringent thresholds with respect to Set 1, while the third set utilizes less stringent thresholds with respect to Set 1.