Segmentation of photospheric magnetic elements corresponding to coronal features to understand the EUV and UV irradiance variability

Abstract

Context. The magnetic field plays a dominant role in the solar irradiance variability. Determining the contribution of various magnetic features to this variability is important in the context of heliospheric studies and Sun-Earth connection. Aims. We studied the solar irradiance variability and its association with the underlying magnetic field for a period of five years (January 2011-January 2016). We used observations from the Large Yield Radiometer (LYRA), the Sun Watcher with Active Pixel System detector and Image Processing (SWAP) on board PROBA2, the Atmospheric Imaging Assembly (AIA), and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Methods. The Spatial Possibilistic Clustering Algorithm (SPoCA) is applied to the extreme ultraviolet (EUV) observations obtained from the AIA to segregate coronal features by creating segmentation maps of active regions (ARs), coronal holes (CHs) and the quiet sun (QS). Further, these maps are applied to the full-disk SWAP intensity images and the full-disk (FD) HMI line-of-sight (LOS) magnetograms to isolate the SWAP coronal features and photospheric magnetic counterparts, respectively. We then computed fulldisk and feature-wise averages of EUV intensity and line of sight (LOS) magnetic flux density over ARs/CHs/QS/FD. The variability in these quantities is compared with that of LYRA irradiance values. Results. Variations in the quantities resulting from the segmentation, namely the integrated intensity and the total magnetic flux density of ARs/CHs/QS/FD regions, are compared with the LYRA irradiance variations. We find that the EUV intensity over ARs/CHs/QS/FD is well correlated with the underlying magnetic field. In addition, variations in the full-disk integrated intensity and magnetic flux density values are correlated with the LYRA irradiance variations. Conclusions. Using the segmented coronal features observed in the EUV wavelengths as proxies to isolate the underlying magnetic structures is demonstrated in this study. Sophisticated feature identification and segmentation tools are important in providing more insights into the role of various magnetic features in both the short-and long-term changes in the solar irradiance.

Solar Soft X-ray Irradiance Variability, I: Segmentation of Hinode/XRT Full-Disk Images and Comparison with GOES (1 – 8 Å) X-Ray Flux

“…Using our algorithm we have separated out the XBPs component from the quiet Sun areas, since quiet Sun is the combination of both background regions (BGs) and XBPs. In the case of EUV and UV image segmentation in our previous studies (Kumara et al, 2014;Zender et al, 2017) we did not separated out the bright points from the quiet Sun component, hence we had to deal with variations of the whole quiet Sun regions. In the present study, we have identified, (separated out background regions and XBPs), and extracted automatically the total number of XBPs based on their intensity levels, morphological structures and sizes (compared to other large scale features like ARs and masking them) over the full-disk images observed in Al_mesh), all this on a daily basis for the period of 13 years (February 2007-March 2020, which covers Solar Cycle 24).…”

“…The long-term irradiance variations are due to the changing emission of bright magnetic elements e.g. (Foukal and Lean, 1988;Kariyappa and Pap, 1996;Worden, White, and Woods, 1998;Kariyappa, 2000Kariyappa, , 2008bVeselovsky et al, 2001;Kumara et al, 2012Kumara et al, , 2014Zender et al, 2017), and the short-term irradiance variations are directly related to active regions as they evolve, fragment, and move across the solar disk (Lean, 1987;Giono et al, 2021;van der Zwaard et al, 2021). The reason for the difference between the observed and modeled solar irradiance variability and the underlying physical mechanisms of solar irradiance variability are not yet fully understood.…”

“…The reason for the difference between the observed and modeled solar irradiance variability and the underlying physical mechanisms of solar irradiance variability are not yet fully understood. Recently some studies have been done to estimate the contribution of various coronal features to EUV and UV irradiance using spatially resolved observations (Kumara et al, 2014;Zender et al, 2017). These authors used full-disk images from PROBA2/SWAP and SDO/AIA and segmented different coronal features to understand the EUV and UV irradiance measured by the PROBA2/LYRA instrument.…”

Segmentation of Coronal Features to Understand the Solar EUV and UV Irradiance Variability III. Inclusion and Analysis of Bright Points

“…A segmentation of solar features into active regions (ARs), coronal holes (CHs), and the quiet Sun (QS), and its application to EUV solar disk images has been presented by Kumara et al (2014) and been extended by Zender et al (2017) to the underlying magnetograms from SDO/HMI. These earlier publications of this study are referenced in the following as Paper I and Paper II, respectively.…”

Empirical Scaling Relations for the Photospheric Magnetic Elements of the Flaring and Non-Flaring Active Regions

“…One of the recent techniques that can track the flux of magnetic features from magnetograms and determines both emergence and cancellation of the magnetic structures is the magnetic ball-tracking algorithm (Attie & Innes 2015). Zender et al (2017) introduced a segmentation method by thresholding on jointing adjacent pixels in a magnetogram. Pérez-Suárez et al (2011) gave a comprehensive review of the automatic detection of solar magnetic features and applications in the space weather.…”