arXiv subjects

E. G. Broock

Publications and source records attributed to E. G. Broock.

3 recordsLinked to original sources

arXiv2026

Detection of resonant nodes in a pore chromosphere

Active region atmospheres host many oscillatory phenomena. The chromosphere is delimited by steep temperature gradients at the photosphere and transition region, where magnetoacoustic waves are trapped and can form standing oscillations within this resonant cavity. We aim to detect the signature of the resonant nodes of standing waves, which are expected to produce sudden jumps in the oscillatory phase and power dips. Spectroscopic temporal series of H$α$ in a pore were acquired with the Swedish Solar Telescope. The velocity and temperature fluctuations at multiple atmospheric heights were inferred from the analysis of the intensity at many spectral positions along the line wings. Wavelet analysis was employed to characterize the phase differences and the power at different heights. The phase shift between velocity and temperature shows a $\pm90^{\circ}$ value, which is consistent with standing oscillations. Robust evidence of the presence of a nodal layer in the temperature at around the height probed by the intensity at H$α\pm0.30$ Å is found, such as the detection of 180$^{\circ}$ jumps in the phase of the temperature oscillations and remarkable power dips at the same atmospheric layer. The exact height of the resonant nodes depends on the spatial location and time. We generally find a mixture of standing and propagating waves. This is consistent with a leaky resonator where waves are partially reflected at the transition region, while some of them are able to propagate into the corona. For the first time, we report the detection and characterization of resonant nodes in the solar chromosphere. This result provides strong observational support for the chromospheric resonant cavity model and paves the way for the development of new seismological techniques to investigate the structure of active region chromospheres.

astro-ph.SR↗

arXiv2022

FarNet-II: An improved solar far-side active region detection method

Context. Activity on the far side of the Sun is routinely studied through the analysis of the seismic oscillations detected on the near side using helioseismic techniques such as phase shift sensitive holography. Recently, the neural network FarNet was developed to improve these detections. Aims. We aim to create a new machine learning tool, FarNet II, which further increases the scope of FarNet, and to evaluate its performance in comparison to FarNet and the standard helioseismic method for detecting far side activity. Methods. We developed FarNet II, a neural network that retains some of the general characteristics of FarNet but improves the detections in general, as well as the temporal coherence among successive predictions. The main novelties are the implementation of attention and convolutional long short term memory (ConvLSTM) modules. A cross validation approach, training the network 37 times with a different validation set for each run, was employed to leverage the limited amount of data available. We evaluate the performance of FarNet II using three years of extreme ultraviolet observations of the far side of the Sun acquired with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of activity. The results from FarNet II were compared with those obtained from FarNet and the standard helioseismic method using the Dice coefficient as a metric. Results. FarNet II achieves a Dice coefficient that improves that of FarNet by over 0.2 points for every output position on the sequences from the evaluation dates. Its improvement over FarNet is higher than that of FarNet over the standard method. Conclusions. The new network is a very promising tool for improving the detection of activity on the far side of the Sun given by pure helioseismic techniques. Space weather forecasts can potentially benefit from the higher sensitivity provided by this novel method.

astro-ph.SR↗

arXiv2021

Performance of solar far-side active regions neural detection

Context. Far-side helioseismology is a technique used to infer the presence of active regions in the far hemisphere of the Sun based on the interpretation of oscillations measured in the near hemisphere. A neural network has been recently developed to improve the sensitivity of the seismic maps to the presence of far-side active regions. Aims. Our aim is to evaluate the performance of the new neural network approach and to thoroughly compare it with the standard method commonly applied to predict far-side active regions from seismic measurements. Methods. We have computed the predictions of active regions using the neural network and the standard approach from five years of far-side seismic maps as a function of the selected threshold in the signatures of the detections. The results have been compared with direct extreme ultraviolet observations of the far hemisphere acquired with the Solar Terrestrial Relations Observatory (STEREO). Results. We have confirmed the improved sensitivity of the neural network to the presence of far-side active regions. Approximately 96% of the active regions identified by the standard method with a strength above the threshold commonly employed by previous analyses are related to locations with enhanced extreme ultraviolet emission. For this threshold, the false positive ratio is 3.75%. For an equivalent false positive ratio, the neural network produces 47% more true detections. Weaker active regions can be detected by relaxing the threshold in their seismic signature. Conclusions. The neural network is a promising approach to improve the interpretation of the seismic maps provided by local helioseismic techniques.

astro-ph.SR↗