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Accessing the fine temporal scale of EUV brightenings and their quasi-periodic pulsations: 1 second cadence observations by Solar Orbiter/EUI

Daye Lim, Tom Van Doorsselaere, Nancy Narang, Laura A. Hayes, Emil Kraaikamp, Aadish Joshi, Konstantina Loumou, Cis Verbeeck, David Berghmans, Krzysztof Barczynski

TL;DR

Using 1 s cadence Solar Orbiter/EUI HRI_EUV data, the study detects over 800k EUV brightenings across active regions and quiet Sun and characterises their lifetimes down to sub-$3$ s, along with QPPs spanning $5$ to $>500$ s. It combines an automated wavelet-based brightenings detector with stationary (AFINO) and non-stationary (EEMD+wavelet) QPP analyses to extract robust signatures across ARs and QS. The results show similar QPP period distributions in ARs and QS, a universal period–lifetime scaling with an exponent around $0.39$, and a weak period–peak-brightness relation, all of which point to flare-like mechanisms operating at small scales and largely independent of large-scale magnetic environment. These findings imply that numerous nanoflares or flare-like processes contribute to coronal heating, and that high temporal resolution is crucial to reveal the full population of short-lived brightenings and their QPPs.

Abstract

Small scale extreme ultraviolet (EUV) transient brightenings are observationally abundant and critically important to investigate. Determining whether they share the same physical mechanisms as larger scale flares would have significant implications for the coronal heating problem. A recent study has revealed that quasi periodic pulsations (QPPs), a common feature in both solar and stellar flares, may also be present in EUV brightenings in the quiet Sun (QS). We aim to characterise the properties of EUV brightenings and their associated QPPs in both QS and active regions (ARs) using unprecedented 1 s cadence observations from Solar Orbiter/Extreme Ultraviolet Imager (EUI). We applied an automated detection algorithm to analyse statistical properties of EUV brightenings. QPPs were identified using complementary techniques optimised for both stationary and non stationary signals, including a Fourier based method, ensemble empirical mode decomposition, and wavelet analysis. Over 500000 and 300000 brightenings were detected in ARs and QS regions, respectively. Brightenings with lifetimes shorter than 3 s were detected, demonstrating the importance of high temporal resolution. QPP periods span from 5 to over 500 s and show similar distributions between AR and QS. We found a consistent power law scaling, with a weak correlation and a large spread, between QPP period and lifetime in EUV brightenings, solar, and stellar flares. The results support the interpretation that EUV brightenings may represent a small scale manifestation of the same physical mechanisms driving larger solar and stellar flares. Furthermore, the similarity in the statistical properties of EUV brightenings and their associated QPPs between AR and QS regions suggests that the underlying generation mechanisms may not strongly depend on the large scale magnetic environment.

Accessing the fine temporal scale of EUV brightenings and their quasi-periodic pulsations: 1 second cadence observations by Solar Orbiter/EUI

TL;DR

Using 1 s cadence Solar Orbiter/EUI HRI_EUV data, the study detects over 800k EUV brightenings across active regions and quiet Sun and characterises their lifetimes down to sub- s, along with QPPs spanning to s. It combines an automated wavelet-based brightenings detector with stationary (AFINO) and non-stationary (EEMD+wavelet) QPP analyses to extract robust signatures across ARs and QS. The results show similar QPP period distributions in ARs and QS, a universal period–lifetime scaling with an exponent around , and a weak period–peak-brightness relation, all of which point to flare-like mechanisms operating at small scales and largely independent of large-scale magnetic environment. These findings imply that numerous nanoflares or flare-like processes contribute to coronal heating, and that high temporal resolution is crucial to reveal the full population of short-lived brightenings and their QPPs.

Abstract

Small scale extreme ultraviolet (EUV) transient brightenings are observationally abundant and critically important to investigate. Determining whether they share the same physical mechanisms as larger scale flares would have significant implications for the coronal heating problem. A recent study has revealed that quasi periodic pulsations (QPPs), a common feature in both solar and stellar flares, may also be present in EUV brightenings in the quiet Sun (QS). We aim to characterise the properties of EUV brightenings and their associated QPPs in both QS and active regions (ARs) using unprecedented 1 s cadence observations from Solar Orbiter/Extreme Ultraviolet Imager (EUI). We applied an automated detection algorithm to analyse statistical properties of EUV brightenings. QPPs were identified using complementary techniques optimised for both stationary and non stationary signals, including a Fourier based method, ensemble empirical mode decomposition, and wavelet analysis. Over 500000 and 300000 brightenings were detected in ARs and QS regions, respectively. Brightenings with lifetimes shorter than 3 s were detected, demonstrating the importance of high temporal resolution. QPP periods span from 5 to over 500 s and show similar distributions between AR and QS. We found a consistent power law scaling, with a weak correlation and a large spread, between QPP period and lifetime in EUV brightenings, solar, and stellar flares. The results support the interpretation that EUV brightenings may represent a small scale manifestation of the same physical mechanisms driving larger solar and stellar flares. Furthermore, the similarity in the statistical properties of EUV brightenings and their associated QPPs between AR and QS regions suggests that the underlying generation mechanisms may not strongly depend on the large scale magnetic environment.

Paper Structure

This paper contains 20 sections, 16 figures.

Figures (16)

  • Figure 1: Representative $\text{HRI}_{\text{EUV}}$ 174 Å images. Images were acquired by Solar Orbiter/EUI on 19 October 2024 at 19:11:08 UT (left) and 19 March 2025 at 11:00:01 UT (right). Top panels: Visualisations from JHelioviewer 2017AA...606A..10M, showing $\text{HRI}_{\text{EUV}}$ images (white rectangles) overlaid with near-simultaneous FSI images. Bottom panels: Full field-of-view $\text{HRI}_{\text{EUV}}$ images, with blue and orange rectangles marking the active region and quiet Sun regions analysed in this study.
  • Figure 2: Examples of the detected EUV brightenings. Panels (a) and (b) show events observed in an active region on 19 October 2024, while panels (c) and (d) show events observed in the quiet Sun on 19 March 2025. In each case, the image corresponds to the time of peak brightness of the event.
  • Figure 3: Logarithmic histograms of EUV brightening properties. The panels show the lifetime (left), surface area (middle), and peak brightness (right) for events detected on 19 October 2024 (top) and 19 March 2025 (bottom). Events detected in active regions and the quiet Sun regions are shown in blue and orange, respectively.
  • Figure 4: Scatter plots showing the relationships between the lifetimes, surface areas, and peak brightnesses of EUV brightenings, detected in the quiet Sun (left panels) and active regions (right panels). The dashed lines represent linear fits on the log-log scale. The correlation coefficient and the slope of each linear fit are indicated in the legend of each panel.
  • Figure 5: Scatter density maps showing the relationship between the lifetimes and surface areas of quiet Sun EUV brightenings detected in this study, combined with the events reported in 2025AA...699A.138N. The colour scale indicates the number density of events per pixel in logarithmic normalisation. Black points correspond to the mean surface area within each of 20 equally spaced bins in the lifetime. The blue dashed line indicates a linear fit to the binned (black) data. The correlation coefficient for the full dataset is 0.31. The correlation coefficient and slope of the linear fit to the binned data, are shown in the legend.
  • ...and 11 more figures