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 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 Jones et al., 2011;Partamies et al., 2022;Royrvik 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 Royrvik 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 Grono et al., 2017;Kaeppler et al., 2023;Kvammen et al., 2020;Nanjo et al., 2022;Rao et al., 2014;Syrjäsuo Tesema et al., 2020;Yamauchi 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, timeresolved 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.Dataset OverviewThe dataset analyzed in this study consists of ground-based auroral optical observations acquired using an Electron Multiplying Charge-Coupled Device (EMCCD) imager operated at Poker Flat, AK (geographic: 65.1°N, 147.4°W; geomagnetic: 65.7°N, 96.6°W; L = 5.9). The observations cover the two-months interval from 1 December 2013 to 31 January 2014, a period characterized by frequent auroral activity during the northern winter season. The EMCCD detector provides high quantum efficiency and low read noise through on-chip electron multiplication, enabling the capture of faint auroral emissions under low-light conditions. The imager recorded continuous 16-bit grayscale image sequences at a fixed exposure time of ~16 ms, operating at 56 frames per second with a narrow 4° field of view and no optical filter. More details on EMCCDs are available at Michell et al., 2014;Michell Tesema et al., 2020). of the ionospheric and of particle including enhancements in changes in ionospheric and (e.g., Miyoshi et al., et al., et al., robust for and numerical models of electron and ionospheric can be with of observed PAs events, enabling on physical and used in (e.g., Kasahara et al., et al., et al., presented in this study is from a single ground-based optical imager in Alaska and a interval during the northern the dataset has the to a of pulsating aurora occurrence across varying associated with or spatial scales. application and of this PAs will of auroral imaging and of the physical pulsating aurora and electron is supported by at through for and is supported by
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