Pulsating aurora (PA) represents a distinct class of diffuse auroral emissions observed in the polar ionosphere, appearing as quasiperiodic intensity modulations or intermittently brightening patches. These structures typically occur at 100 km altitude have horizontal scale size ranging from 10 to 200 km (Hosokawa and Ogawa, 2015; McEwen et al., 1981; Yukitoshi et al., 2020) and exhibit repetition periods ranging from a few seconds to several tens of seconds (Yamamoto, 1988). PAs are most commonly found in the post-midnight sector to dawn sector (Grono and Donovan, 2020; Jones et al., 2011; Partamies et al., 2022; Royrvik and Davis, 1977) usually near the equatorward edge of the auroral oval and are most often associated with the recovery phase of substorms (Bland et al., 2020; Tesema et al., 2020; Tsuchiya et al., 2018). PAs are spatially patchy, often drift slowly in the ionosphere, and exhibit complex temporal modulation patterns including on–off pulsations, drifting patches, and internal substructures (Grono and Donovan, 2020; Royrvik and Davis, 1977; Yukitoshi et al., 2020). PAs arises from quasiperiodic precipitation of magnetospheric electrons with characteristic energies of tens of keV. These electrons are modulated prior to entering the upper atmosphere (Johnstone, 1978; Samara et al., 2010; Sandahl et al., 1980). PAs are widely interpreted as the ionospheric manifestation of pitch-angle scattering of tens-of-keV electrons in the magnetosphere by very-low-frequency (VLF) and whistler-mode chorus waves (e.g., Jaynes et al., 2015; Kasahara et al., 2018; Nishimura et al., 2010). In this study, a pulsating aurora is defined as a spatially localized auroral emission that exhibits quasi-periodic, discrete intensity enhancements in time, identifiable as significant peaks relative to the local background intensity. From an algorithmic perspective, a pulsating aurora event is characterized by (i) a clear intensity pulse exceeding a prescribed prominence threshold, (ii) a finite pulse duration consistent with reported pulsation timescales (seconds to tens of seconds), and (iii) temporal separation from adjacent pulses that distinguishes individual pulsations from steady or slowly varying diffuse aurora.

Historically, the identification and classification of PAs have relied heavily on manual inspection of: (i) ground-based all-sky imager data: (e.g., Shiokawa et al., 2010; Yang et al., 2015). (ii) Satellite optical data: (e.g., Klimov et al., 2022; Siren, 1975) (iii) Energetic particle measurements: (e.g., Miyoshi et al., 2015; Kasahara et al., 2018). While manual inspection–based identification of pulsating aurora is often accurate, it is inherently subjective and does not scale to modern data volumes. However, auroral observation networks such as Time History of Events and Macroscale Interactions during Substorms (THEMIS) ground-based All-Sky Imager (ASI) array, Magnetometers, Ionospheric Radars, All-sky Cameras Large Experiment (MIRACLE), and Red-line Emission Geospace Observatory (REGO) now generate terabyte-scale datasets spanning multiple years and both hemispheres. For example, the THEMIS all-sky imager network alone has accumulated tens to hundreds of millions of images, making manual inspection impractical. As a result, manual identification is time-consuming, observer-dependent, and susceptible to bias arising from individual experience and selection criteria. These limitations have driven the development of automated and semi-automated detection techniques, including image thresholding, frequency-domain analyses, machine-learning–based classifiers, and morphological feature-tracking methods (e.g., Clausen and Nickisch, 2018; Grono et al., 2017; Kaeppler et al., 2023; Kvammen et al., 2020; Nanjo et al., 2022; Rao et al., 2014; Syrjäsuo and Donovan, 2002; Tesema et al., 2020; Yamauchi and Brändström, 2023; Zhong et al., 2020). Despite these advances, no single detection framework has yet emerged as a broadly applicable standard across different datasets or observation platforms.

Many traditional threshold-based methods are highly sensitive to imager-specific characteristics such as camera gain, exposure, and background illumination conditions, which limits their transferability across imaging systems. Machine-learning-based approaches, while powerful, typically require large, carefully labeled training datasets and often behave as black-box classifiers, making it difficult to interpret failure modes or adapt the models to new imager without retraining. Several methods also rely on fixed spatial or temporal scales, which can lead to missed detections or false positives when auroral dynamics deviate from assumed scales. Moreover, a new approach is particularly required for the Alaska imager used in this study, for which a dedicated pulsating aurora detection framework has not yet been established. Thus in this study, we develop and present an open-source Python software package for the automated, time-resolved detection of PAs using ground-based optical imager observations. The scripts are designed to automatically load raw imager TIFF files and integrate spatial segmentation of auroral images to produce standardized, time-resolved pulsation products that are well suited for large-scale statistical analysis. Rather than focusing on individual events, the framework is optimized for survey-style analysis, enabling users to move from raw image sequences to curated lists of pulsation intervals. The paper is organized as follows. Section 2 details dataset overview. Section 3 describes the datasets and pre-processing methods and details of the automated detection algorithm. Section 4 presents visual overview of example events, statistical results and dataset value. Section 5 is summary.

Figure 1 shows three examples of pulsating aurora events detected by the automated algorithm. Panel (a) shows a two-dimensional all-sky imager frame at the indicated UT time. The overlaid boxes represent the predefined analysis regions used to compute average intensities. Red boxes denote regions without pulsating aurora or without peak pulsation activity, while the white box highlights the region in which the algorithm identifies the maximum pulsating auroral intensity across the entire imager field of view. In this example, a bright red patch corresponding to pulsating aurora is detected, with its maximum intensity occurring in region 15 (white box). Panel (b) shows the corresponding grid-averaged intensity-versus-time profile for this region and illustrates the key pulsation parameters identified by the algorithm. Panel (c) presents a second example of pulsating aurora characterized by a streak-like feature across the imager field of view. This feature is detected in region 9 (white box), and the corresponding intensity-versus-time plot in panel (d) shows a sharp, short-duration pulse. Panel (e) shows a third and particularly interesting example. Although a bright auroral patch appears near the center of the imager frame and represents the brightest feature in the scene, it is relatively steady and does not exhibit pulsations. In contrast, a weaker pulsating aurora is detected in region 8 (white box). The algorithm successfully identifies this lower-intensity pulsation despite the presence of a brighter, non-pulsating feature in the same frame, demonstrating the robustness of the region-based detection approach. The corresponding intensity-versus-time profile for this event is shown in panel (f). The video of these interesting pulsating aurora events is attached on zenodo.

Figure 2 presents the histogram of the daily number of detected pulsating aurora events over the analysis interval. In total, 10,567 pulsating aurora events were identified by our automated algorithm. The missing dates on the histogram correspond to days when no pulsations were observed. The event counts vary by more than three orders of magnitude, with particularly high occurrence rates on 25 December 2013 and several days in early January 2014. To clearly display both high- and low-activity days, the y-axis is shown on a logarithmic scale. Days with low event counts, such as 30–31 December 2013 and 13 January 2014, are still clearly resolved, highlighting the strong day-to-day variability in pulsating aurora occurrence during this period. A detailed scientific analysis of these events will be presented in a forthcoming paper. In the present study, we release the complete list of detected events.

In this work we focused on the relative intensity of the photons to identify PAs and not the absolute values of the count. Even within the narrow field-of-view system, optical distortions arising from lens curvature introduce spatially dependent shifts in intensity and localization, affecting the precise identification of peak auroral emissions across the image, including within the central approximately 4-degree field of view. These distortions become increasingly pronounced toward the edges of the imager and influence both the measured intensity and the inferred spatial structure of PAs. For this reason, the current detection framework applies region-dependent thresholds when dividing the imager observations into multiple spatial grids, accounting for center-to-edge variations in sensitivity and distortion. In future work, we will apply flat-field calibration procedures to correct for center-to-edge sensitivity variations in the auroral imager. This calibration will enable more accurate spatial mapping and improve the robustness of pulsation event characterization across the entire field of view. At present, identical auroral intensities can appear weaker toward the edges of the imager (e.g., an intensity of 1.0 at the center may appear as 0.5 at the edge despite representing the same physical brightness). Flat-field correction will normalize these variations by appropriately scaling edge intensities, thereby producing a uniform response across the full field of view. This uniformity will allow us to apply a single, consistent prominence threshold across the entire imager, rather than adjusting detection parameters spatially, as was required in the current implementation. These corrections will enhance the reliability of the automated detection framework and support its application to long-duration datasets and future auroral imaging campaigns. Adding flat-field corrections will more easily make these algorithms applicable to data from larger FOV imaging systems, such as all-sky cameras, and to filter imager data, where absolute photometry measurements are possible.

The pulsating aurora event database presented in this study enables a broad range of investigations spanning the magnetosphere, ionosphere, and upper atmosphere. Progress in understanding PAs has historically been constrained by the absence of large, objectively identified event catalogs. This database addresses that limitation by providing automatically detected, time-resolved PAs intervals derived from ground-based all-sky imager observations. Key scientific applications of this database include:

Statistical studies of PAs occurrence rates, spatial distribution, and temporal characteristics as functions of magnetic local time, geomagnetic activity, background auroral conditions, and solar wind driving. (e.g., Lessard, 2012; Jones et al., 2011; Grono and Donovan, 2020).

The database presented in this study is derived from a single ground-based optical imager located in Alaska and covers a two-month interval during the northern winter. Consequently, the dataset has the limitation to provide a statistically comprehensive representation of pulsating aurora occurrence across seasons, varying geomagnetic conditions associated with solar cycle variability, or extended spatial scales. Overall, continued application and expansion of this PAs database will facilitate systematic exploitation of growing auroral imaging archives and significantly advance understanding of the physical processes governing pulsating aurora and energetic electron precipitation.