To resolve intensity modulations at frequencies of a few hertz, high-speed imaging is required. We used an ORCA-Quest quantitative CMOS (qCMOS) camera (Hamamatsu Photonics) equipped with a Kowa LM8HC lens (F1.4, f=8 mm). The camera was operated at 20 Hz, which satisfies the Nyquist criterion for the targeted modulation frequencies.

The native sensor format is 4096 × 2304 pixels; however, on-board hardware binning was applied to reduce the effective image size to 1024 × 576 pixels. The resulting field of view (FoV) is 76° in the horizontal direction. A BG3 glass filter was placed in front of the lens to block the strong forbidden lines at 557.7 and 630.0 nm, while retaining sensitivity to the N2+ first negative and N₂ first positive band systems. This configuration was chosen to improve the SNR by emphasizing broadband auroral emissions rather than a specific emission line.

The same camera systems with the same optical configuration and acquisition settings were deployed at Kiruna, Sweden (67.841° N, 20.411° E), Abisko, Sweden (68.355° N, 18.819° E), and Skibotn, Norway (69.348° N, 20.363° E). The Abisko dataset is limited because the deployment started later than at the other sites. In addition, the FoV overlap between Kiruna and Abisko is substantial, which could lead to double counting of the same auroral structures in statistical analyses. Therefore, Abisko data are excluded from the statistics presented in this study, and we focus on observations from Kiruna and Skibotn. The Kiruna dataset used in this study covers October 2024 through December 2025, whereas the Skibotn dataset covers October 2023 through December 2025. These two sites are located at nearly the same longitude but at different latitudes, and their FoVs do not overlap when projected to an emission altitude of 100 km. Over the observation period, Kiruna and Skibotn correspond to AACGM latitudes of approximately 65.1 and 66.7°, and L values of approximately 5.6 and 6.4, respectively, noting that magnetic coordinates are time dependent. The FoVs of the two sites projected to 100 km altitude are shown in Fig. .

In addition to the qCMOS cameras, all-sky commercial digital cameras were operated at both Kiruna and Skibotn and used to provide complementary observations. In Kiruna, a Sony α7S equipped with a Nikkor 8 mm F2.8 lens was installed in the same building as the qCMOS camera; the camera was operated at ISO 4000 with an exposure time of 4–13 s, which was automatically adjusted depending on sky brightness. At Skibotn Observatory, a Sony α6400 with a MEIKE MK-6.5 mm F2.0 lens was used at ISO 8000 with an exposure time of 8 s. In Skibotn, the nominal imaging cadence is 1 min, but it can increase to 20 s when a near-real-time deep-learning image classifier detects auroral activity . These color all-sky images are used here for pre-selection of the data and to provide wider morphological context.

The presence or absence of IM was determined automatically from keograms using supervised image-classification models based on a ResNet-18 backbone initialized with ImageNet-pretrained weights. As a pre-selection step, we used color all-sky camera images acquired at Kiruna and Skibotn. These images were independently classified for each site by the Tromsø AI system , which is based on a ResNet-50 backbone and outputs probabilities for eight classes: Arc, Discrete, Diffuse, Aurora but cloudy, Aurora but bright, Clear, Cloudy, and Dusk and Dawn. Model performance is reported in , with an average precision of 93.1 % and an F1 score of 93.4 %. Here, the F1 score is defined as F1=2PR/(P+R), where P and R denote precision and recall, respectively.

To reduce computational cost, the Tromsø AI classifications were used only as a broad pre-screen for auroral activity. Minute-level intervals were retained when p(Arc)+p(Discrete)+p(Diffuse)≥0.8 and the corresponding qCMOS data were available. This criterion was adopted because the color all-sky cameras and their acquisition settings differed between the two sites, and therefore the boundaries among the Arc, Discrete, and Diffuse classes may not have been identical. For each retained minute, we generated a 1 min keogram from the 20 Hz qCMOS image sequence. The keogram was constructed from a north–south slice through the image center, resulting in a size of 1200 pixels in the time direction and 576 pixels in the spatial direction.

The Tromsø AI output was not used to determine the presence of PsA or IM. The manually annotated intervals retained their human labels, whereas all the remaining intervals were evaluated using the hierarchical PsA–IM classifier described below. Thus, intervals pre-screened as Arc or Discrete could enter the initial sample, but they contributed to the denominator of p(IM∣PsA) only when they were subsequently classified as PsA-positive.

The above pre-screening procedure produced 30 464 keograms for Kiruna and 24 474 keograms for Skibotn, corresponding to 507.7 and 407.9 h of data, respectively. Although the nominal observation period was longer at Skibotn, fewer keograms were available because the availability of archived raw qCMOS data was intermittent. Major intervals during which all pre-screen-eligible minutes lacked corresponding raw data included 20–26 October 2023, 9–13 November 2023, 13–22 December 2023, 31 December 2023–5 January 2024, 9–16 September 2024, 31 December 2024–3 January 2025, and 4–6 March 2025. The Kiruna dataset consisted of 12 429 manually annotated keograms and 18 035 additional keograms evaluated by the model, whereas all 24 474 Skibotn keograms were evaluated by the model. For each keogram, MLT and MLAT were calculated using the geographic coordinates of the corresponding observation site and the observation time.

IM was detected using a two-stage hierarchical binary classification. First, a classifier determines whether a keogram contains PsA. Second, for keograms classified as containing PsA, a classifier determines whether IM is present. The definitions below were used to create supervised training labels for both stages. Because keograms are constructed over 1 min, we did not treat pulsations with repetition periods ≥ 30 s as PsA. IM was defined as the presence of intensity modulations at frequencies of a few hertz within keograms containing PsA. If both internally modulated and non-modulated patches coexist within a single keogram, the keogram was labeled as IM only when a majority of the pulsation patches exhibited IM.

Based on these definitions, we manually annotated 12 429 Kiruna keograms (98 nights) observed from October 2024 to September 2025. Of these, 243 keograms (2.0 %) were labeled as ambiguous (“?”) and excluded from model development, leaving 12 186 keograms for training/validation/testing. In this set, 8163 keograms from 90 nights were labeled as containing PsA (PsA = 1), and 2608 from 72 nights were labeled as containing IM within PsA (IM | PsA = 1).

Figure  shows representative examples of 1 min keograms used to illustrate the criteria adopted for creating the training labels, together with line plots extracted from selected horizontal bands in two of the keograms. Panels (a)–(c) are 1 min keograms constructed from a 20 Hz qCMOS image sequence. The horizontal axis represents time over the 1 min interval, while, in panels (a)–(c), the vertical axis shows pixel position along a north–south slice through the image center, with north at the top and south at the bottom, and grayscale indicates the raw count value. Panels (a') and (b') show the raw count values averaged over the narrow horizontal bands indicated by the dashed lines in panels (a) and (b), respectively. The title above panels (a), (b), and (c) gives the start and end times (UT) of the corresponding 1 min interval.

Figure a shows an example in which PsA is present but IM is not identified. Representative PsA structures without IM are indicated by the arrows. The corresponding line plot in Fig. a' shows relatively gradual intensity variations associated with the larger-scale patches, without clear modulations at frequencies of a few hertz. Figure b shows an example in which PsA is present and rapid intensity modulations (IM) are clearly embedded within its brighter phase. Representative regions in which IM is visible are indicated by the arrows. These rapid modulations are more clearly visible in the corresponding line plot in Fig. b'. In contrast, Fig. (c shows an example in which temporal variability is visible, but the morphology corresponds to flickering in discrete aurora; under the labeling scheme adopted in this study, it is therefore not classified as PsA. The arrow indicates a representative part of this discrete auroral structure.

Thus, Fig.  demonstrates that the labeling was not based simply on the presence or absence of temporal variation of any auroras, but instead distinguished between (i) the presence or absence of PsA and (ii) whether IM at frequencies of a few hertz was present when PsA was present. In particular, Fig. c shows that even when IM-like variation is visible, some events are excluded from PsA on the basis of their morphological context. This means that the training labels used in this study were defined not only by frequency-related features but also by morphological information.

We used a hierarchical two-stage binary classification model that takes 1 min keograms as input. The first stage determines the presence or absence of PsA, and the second stage determines the presence or absence of IM conditional on PsA = 1. Hereafter, the performance of the first stage is denoted as PsA F1, and that of the second stage as IM | PsA F1. For the second stage, IM | PsA precision and IM | PsA recall were also evaluated.

The labeled keograms were split into training, validation, and test sets at the night level, such that keograms from the same night were never assigned to different subsets. This was done to avoid overly optimistic performance estimates caused by temporal correlation among neighboring keograms within a night. Here, an observing night was defined as the interval from 14:00 to 09:00 UT on the following day. For the fixed split used in the main analysis, the 98 nights were randomly assigned to the training, validation, and independent test sets, yielding 9398 keograms from 78 nights for training, 983 keograms from 10 nights for validation, and 1805 keograms from 10 nights for testing. The test set contained 1222 PsA-positive keograms from nine nights, including 432 keograms labeled as containing IM within PsA, also from nine nights. The test set was not used for model training, model selection, or early stopping and was used only for final performance evaluation.

Figure  summarizes the results of 10 training and evaluation runs performed under a fixed night split, with only the random seed varied. These 10 runs therefore share the same data split, and the differences among them mainly reflect variations arising from model initialization and training order. Accordingly, Fig.  is intended to assess reproducibility with respect to training randomness, rather than variability due to differences in data splitting. Variability associated with different night splits is examined separately in Appendix A.

Figure a shows the learning curves on the validation data. The thin pale lines indicate the curves for each of the 10 random seeds, whereas the thick lines indicate the run adopted for the analysis. Blue represents PsA F1 and red represents IM | PsA F1, and the red circle marks the epoch at which IM | PsA F1 reached its maximum in the adopted run (best epoch = 8). In this study, the validation IM | PsA F1 was used as the monitoring metric for model selection and early stopping. An epoch was regarded as an improvement only when this metric exceeded its value at the previous epoch, in which case the best model was updated. Training was stopped when the number of consecutive epochs without improvement reached four, and the test evaluation after training was always performed using the best epoch model rather than the weights from the final epoch. Therefore, the curves in the later epochs of Fig. a illustrate the behavior of the training process, whereas the test-performance values correspond to the best epoch selected on the basis of validation IM | PsA F1.

PsA F1 remains consistently high overall, with only small variation among random seeds. In contrast, IM | PsA F1 exhibits larger variability. This likely reflects the greater ambiguity of IM labeling: IM is a relatively subtle phenomenon with lower amplitude, and a keogram is labeled as IM-positive only when internally modulated PsAs are judged to occupy the majority of the pulsating structures, making the boundary less clear even for human inspection. Note that the maximum number of epochs shown in the figure is affected by the early-stopping criterion described above, and therefore fewer runs are shown in the later epochs.

Figure b shows the distribution of test performance across the same 10 random seeds. PsA F1 is high, with a mean of 0.9506 (SD = 0.0071), indicating stable performance across seeds. In contrast, IM | PsA F1 has a mean of 0.7472 (SD = 0.0201), and its precision and recall show relatively larger variation, with mean values of 0.6630 (SD = 0.0500) and 0.8630 (SD = 0.0407), respectively. In other words, although the precision–recall balance of IM classification can vary with random seed, the overall F1 remains within a relatively narrow performance range. The test performance of the adopted run (PsA F1 = 0.9401, IM | PsA F1 = 0.7460, IM | PsA precision = 0.6847, IM | PsA recall = 0.8194) falls within the distribution of the 10-seed results in all metrics, indicating that the selected run is not an exceptional one.

Figure b also includes reference values for the IM-related metrics based on a weighted evaluation derived from visual review of the test-set misclassifications (orange points). This supplementary evaluation was introduced to reflect the quality of the misclassifications, distinguishing between clear model errors, borderline cases, and cases in which the ground-truth label itself may deserve reconsideration, rather than relying solely on strict binary scoring. Specifically, all 241 IM misclassifications in the independent test set produced by the adopted model (163 false positives and 78 false negatives, distributed over nine nights) were visually inspected, and the misclassifications were grouped into three categories. Heavy cases were assigned a weight of 1.0 and represent clear model errors, for example when IM is evident throughout the keogram but the model predicts non-IM, or vice versa. Borderline cases were assigned a weight of 0.5 and represent examples near the labeling boundary that are difficult even for human inspection, such as cases with weak modulation or cases in which IM-positive and IM-negative structures appear in roughly equal proportions within the image. Label-questionable cases were assigned a weight of 0.25 and represent examples for which the ground-truth label itself may be open to revision, making it difficult to regard the model output as a clear failure. Recomputing IM | PsA precision, recall, and F1 using these weights yielded IM | PsA F1 = 0.8290, IM | PsA precision = 0.7629, and IM | PsA recall = 0.9077, all higher than the corresponding values from the standard binary evaluation (0.7460, 0.6847, and 0.8194, respectively). These results suggest that a non-negligible fraction of the IM misclassifications arises from cases near the definition boundary or from uncertainty in the reference labels.

Based on the above, we conclude that the model used in this study shows high reproducibility and stability for PsA detection. Although IM detection is accompanied by some uncertainty arising from boundary cases, its performance is sufficient for statistical analysis when interpreted together with the associated error assessment.

As shown in the previous section, PsA classification exhibited high reproducibility, and IM classification, although subject to some uncertainty arising from boundary cases, achieved performance sufficient for statistical analysis when interpreted together with the corresponding error assessment. We therefore applied the adopted model to unseen data to examine the MLT/MLAT dependence of IM occurrence. Figure  first summarizes the classification results in MLT bins, allowing comparison between the manual labels and model inference, as well as an overall assessment of site-to-site differences.

Figure  shows the number of 1 min keograms in each MLT bin, displayed as stacked bars for the categories IM=1∧PsA=1, IM=0∧PsA=1, and PsA=0 (with an additional “?” category in the manual labels for unclassifiable cases). Figure a shows the manual labels for Kiruna, Fig. b shows the model inference for unseen data from Kiruna, and Fig. c shows the model inference for unseen data from Skibotn. The model inference follows a hierarchical decision scheme: cases with p(PsA)≥0.5 are classified as PsA-positive, and for those classified as PsA-positive, cases with p(IM)≥0.5 are further classified as IM-positive.

This figure shows the number of images in each class, which is equivalent to the number of minutes because each image corresponds to a 1 min interval. The bar heights therefore directly reflect differences in sample size among the MLT bins. In particular, the earliest and latest MLT bins tend to contain fewer samples because the length of nights (dark time) varies seasonally, limiting coverage at those local times. Accordingly, the class composition should be interpreted together with the total sample size in each bin. In addition, whereas the manual labels include a reserved category for uncertain cases (“?”), the model outputs probabilities for all samples and therefore does not produce a “?” category.

Figure b and c also include count-based uncertainty estimates derived from the misclassification rates obtained from the test data, which is explained in Sect. 3.2. The black markers indicate ranges based on the uncorrected precision and recall (strict), shown as blue points in Fig. b, whereas the orange markers indicate weighted ranges that incorporate the visual review of IM misclassifications, shown as orange points in Fig. b. This allows the robustness of the observed trends to be assessed not only by direct comparison of bar heights, but also by taking classification errors into account. Specifically, for each MLT bin, let the model-predicted counts be denoted by NIM for IM=1∧PsA=1, NPsA for IM=0∧PsA=1, and N0 for PsA=0. Asymmetric uncertainties were then assigned by applying the false-positive and false-negative rates derived from the test data, denoted by ϵFP and ϵFN, to the corresponding counts. For the top of the IM layer (the top of the red segment), we use 1ΔNIM-=NIMϵIMFP,ΔNIM+=NPsAϵIMFN.

That is, the lower uncertainty is given by the IM false-positive rate, and the upper uncertainty by the IM false-negative rate. Furthermore, for the top of the PsA=1 layer (red + blue; shown only for the strict case), we define NPsA1=NIM+NPsA and use 2ΔNPsA1-=NPsA1ϵPsAFP,ΔNPsA1+=N0ϵPsAFN.

Accordingly, the markers in Fig. b and c do not represent statistical confidence intervals, but rather sensitivity-based uncertainty ranges in count space obtained by propagating misclassification rates to the observed counts.

Qualitatively, for Kiruna, the manual labels (Fig. a) and the model inference (Fig. b) share broadly consistent trends in the MLT-dependent changes in class composition. Note that the model-inference dataset for Kiruna (Fig. b) contains more samples than the manually labeled dataset (Fig. a), and therefore differences in total bar height partly reflect differences in sample size. Nevertheless, even taking this into account, both show a relative increase in the contribution of IM=1∧PsA=1 on the morning sector. In contrast, at Skibotn (Fig. c), even with the same model and decision criteria, the relative contribution of IM=0∧PsA=1 is larger than at Kiruna. This suggests that both physical differences between the regions sampled by the two sites and differences in observational conditions may influence the occurrence characteristics of IM. Overall, Fig.  demonstrates that the main trends identified from the manual labels are preserved, while the model inference enables expansion of the sample size.

Figure  shows the classification counts in each MLT bin, but because the total number of samples (observing minutes) differs substantially among bins, the occurrence tendency of IM is better compared in terms of its fraction within PsA cases. Therefore, Fig.  compares the fraction of IM = 1 cases among PsA cases in each MLT bin (hereafter, the IM fraction within PsA) between Kiruna and Skibotn.

Figure a shows the IM fraction within PsA obtained by combining the manually labeled data and the model-inference data for Kiruna, whereas Fig. b shows the corresponding result derived solely from the model-inference data for Skibotn. Each bar represents the composition within cases classified as PsA=1, where the height of the red segment (IM=1∧PsA=1) corresponds to the IM fraction within PsA, and the blue segment (IM=0∧PsA=1) represents the remainder. Model classification was performed using the same hierarchical decision scheme as in Fig.  (first p(PsA)≥0.5, and then, for cases classified as PsA-positive, p(IM)≥0.5). MLT bins 17, 18, and 7 were excluded from this comparison due to the small number of samples and the resulting low stability of the ratio estimates. Across the MLT bins included in Fig. , the conditional ratios were calculated from 14 796 PsA-positive minutes at Kiruna and 7013 PsA-positive minutes at Skibotn.

The black (strict) and orange (weighted) error bars shown in the figure represent approximate uncertainties obtained by mapping the false-positive and false-negative rates of the IM classification into the ratio space. For Skibotn, uncertainties were propagated over the entire dataset because all keograms were classified by the model. In contrast, for Kiruna, the manually labeled portion was treated as fixed, and uncertainties were applied only to the model-inferred portion that was added. Therefore, the error bands for Kiruna primarily reflect the uncertainty associated with the model-derived component, rather than the uncertainty of the combined estimate as a whole. As a result, both sites show an increase in the IM fraction within PsA toward the morning-sector MLT. However, when compared at the same MLT, the values at Kiruna are higher than those at Skibotn in most bins, and this difference becomes more pronounced toward the morning sector.

To make this difference more explicit, Fig.  shows the site-to-site comparison of the IM fraction (Kiruna - Skibotn) for each MLT bin. Positive values indicate that the IM fraction at Kiruna is higher than that at Skibotn at the same MLT. As in the previous figures, the black error bars indicate difference ranges based on the strict uncertainties, whereas the orange error bars indicate difference ranges based on the weighted uncertainties. The difference ranges were obtained by worst-case combination of the upper and lower bounds for Kiruna and Skibotn separately (lower bound = lower bound at Kiruna - upper bound at Skibotn; upper bound = upper bound at Kiruna - lower bound at Skibotn). The gray shading in the figure indicates MLT bins for which the strict uncertainty range does not include zero.

Figure  shows that, from the evening to midnight sector (19:00–03:00 MLT; MLT = 0 corresponds to approximately 22:00 UT), the site-to-site differences remained within the strict uncertainty ranges that include zero, and no significant difference was identified. In contrast, on the morning sector (04:00–06:00 MLT), the IM fraction at Kiruna was systematically higher, with central differences of 0.293, 0.390, and 0.221 at 04:00, 05:00, and 06:00 MLT, respectively. The corresponding strict ranges exclude zero at 04:00 MLT (0.058–0.403) and 05:00 MLT (0.120–0.541), whereas the strict range at 06:00 MLT (-0.002–0.452) marginally includes zero. Taken together, these results indicate that, in the morning sector, PsA was more likely to be accompanied by IM at the lower-latitude site (Kiruna) than at the higher-latitude (Skibotn).

Figures – showed the MLT dependence of IM occurrence and its site-to-site (MLAT) dependence by summarizing the model classification results. In contrast, Fig.  shows a single-night example from evening to morning, with the keogram by the color all-sky camera and model probabilities displayed together to illustrate the tendencies seen in the statistical results at the event level. The example is from Kiruna and spans the evening of 20 November 2025 to the morning of 21 November 2025. The observations from this night were not used for model training. Figure a shows the keogram constructed from the color all-sky images. Figure b shows the probability of existence of PsA, p(PsA), and Fig. c shows the conditional probability of IM under PsA, p(IM∣PsA). Because the model probabilities were computed from keograms generated at 1 min intervals, the time resolution is 1 min. In addition, because the IM features were learned only from images containing PsA, p(IM∣PsA) is plotted as missing (NaN) at times when p(PsA)<0.5. This night was selected because auroral activity persisted throughout the night, allowing the evening-to-morning evolution to be illustrated continuously.

The keogram in Fig. a provides an overview of the temporal evolution of auroral activity during this night. Around 18:30 UT, discrete arcs appeared on the northern side, and from about 19:00 to 19:30 UT, structures propagating from north to south were observed. After that, faint diffuse aurora persisted for several hours. Around 21:40 UT, an auroral breakup was observed on the southern side, partly accompanied by red emissions suggestive of a contribution from the 630.0 nm OI line, and bright discrete aurora continued until around 23:00 UT. Thereafter, toward the morning sector, diffuse aurora again persisted until the dawn.

Comparing Fig. a with Fig. b and c, it can be seen that PsA appeared after both of two discrete auroras, whereas IM became prominent only after the latter discrete aurora (the auroral breakup). In addition, after the breakup, PsA maintained a high probability until the dawn, whereas p(IM∣PsA) showed more temporal variability. In other words, the IM probability did not remain constantly high: after an initial increase it decreased once, then rose again around 02:20 UT, and became more persistently elevated after about 02:50 UT.

The vertical dotted lines labeled A–D in the figure mark reference times on the keogram that are examined in Fig.  (A: 19:26 UT, B: 23:45 UT, C: 01:54 UT, D: 03:22 UT). At all of these times, p(PsA)=1.0, indicating strong support for the presence of PsA, whereas p(IM∣PsA) differs from one time to another. Specifically, it is low at A and C (A: 0.000, C: 0.002) and high at B and D (both 1.000).

Figure  presents, for the representative times A–D shown in Fig. , the 1 min keograms from the qCMOS camera used as input to the model (left column) together with the corresponding color all-sky images from Kiruna, in which the qCMOS FoV is embedded (right column). This allows us to examine how the variations in the probability time series shown in Fig.  correspond to the actual data.

A (19:26 UT) corresponds to a case in which the model assigns a high probability to PsA and a low probability to IM (Fig. ). In the qCMOS keogram (left), stable patches brighten and dim with a period of about 10 s, indicating clear main pulsations, whereas little IM is visible within them. In addition, in the patch region below the white dotted line, the background brightness remains higher than in the upper part even during the darker phases of the main pulsation, suggesting superposed background emission. In the color all-sky image, diffuse aurora dominates within the qCMOS FoV, and patch boundaries are not so clear in the single-frame image.

B (23:45 UT) corresponds to a case after the auroral breakup, for which the model assigns high probabilities to both PsA and IM. In the qCMOS keogram, as indicated by the white arrows, typical IM at about 2–3 Hz can be identified at multiple positions, embedded within the bright phase of the main pulsation. Although the main-pulsation period itself is comparable to that in A, there is a clear difference in morphology: in A, the brightening and dimming occurred relatively repeatedly at similar positions, whereas in B, the pulsating regions are distributed over multiple y positions (i.e., latitudes) within the FoV and appear spatially more unstable. As in A, the white dotted line indicates that background emission is superposed on the patch region (y-axis). In the color all-sky image, diffuse aurora is broadly distributed, with some patchy structures visible particularly on the lower-latitude side. Within the qCMOS FoV, a complex pattern is observed, and the patch boundaries are not very distinct.

C (01:54 UT) corresponds to a case in which the model assigns a high probability to PsA and a low probability to IM. In the qCMOS keogram, main pulsations are observed over a wider portion than in A and B, and the darker phase is shorter. In addition, as indicated by the white arrows, a few localized intensity modulations at around 1 Hz are present. However, unlike in B, these modulations appear as finer fragmentations of the main pulsation, rather than being clearly embedded within its bright phase. The background emission seen in A and B appears weaker in this case. In the right panel, the edges of patches are more clearly defined than in A and B, with a more distinct contrast between bright and dark regions.

D (03:22 UT) corresponds to a case in which the model assigns high probabilities to both PsA and IM, and the qCMOS keogram shows intensity modulations at about 1–2 Hz over a wide portion. Similar to the modulation seen in part of C, these fluctuations do not appear as modulations clearly embedded within the bright phase of the main pulsation. Rather, paradoxically given the terminology, the IM component itself appears to be dominant. In addition, the overall brightness during this interval was lower than in A–C (see the difference in color scale). The right panel shows an overall faint emission state, although some localized patch-like boundaries are still visible.

Overall, the comparison between the qCMOS keograms and the snapshot images in Fig.  shows that the model outputs in Fig.  (the time series of PsA/IM probabilities) are broadly consistent with the visually identifiable temporal structures, at least for this example. In other words, the model reasonably distinguishes intervals dominated by PsA from those in which IM is prominent. At the same time, even within events classified as the same PsA or IM category, there is clear diversity in the spatial structure of patches, the degree of background emission, the manner in which main pulsations and IM are superposed, and the overall brightness.