Sustained kink oscillations in coronal loops have long been observed in TRACE, SDO/AIA, and more recently in SolO/EUI images. Although their properties are quite well-known now, their driver and excitation mechanism remain under active debate. In this talk I will give an overview over the different ideas/theories that discuss the role of photospheric driving in the generation of kink oscillations. We exploited an unique dataset of high-resolution coronal and photospheric observations taken recently by SolO/EUI/HRI and the Swedish 1-m Solar Telescope (SST) respectively during a dedicated coordinated campaign run in October 2023. Using the SST/CRISP data we estimated and quantified the strength of photospheric driving at the footpoints of active region coronal loops, that include pore, plage, enhanced-network and sunspot regions. We then looked at kink oscillation signatures in the same coronal loops within the EUI/HRI coronal images. An attempt was then made to link the photospheric and coronal results together. I will finally discuss the implications of this work on the driving and excitation mechanism of kink oscillations, and future perspectives.
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Solar filaments (also called prominences when seen off-disk) are solar atmospheric structures consisting of dense, cool plasma clouds floating within the sun's corona. Since the beginning of solar observations, it has been seen that the prominences oscillate with a wide variety of motions. These periodic motions are very common, but there are no systematic studies of these oscillations. It has recently been shown that spectral analysis of solar filaments is a powerful tool to identify oscillations in these structures. With this technique, the power spectral density (PSD) is calculated for each pixel of the Halpha images. To differentiate between a detection or a spurious oscillation, it is necessary to determine the background noise. We have seen that this background noise is a combination of red and white noise. The red-noise nature of the PSD is problematic in their study since most of the statistical tools developed to identify real oscillations from the noise are for white-noise PSD. The most appropriate approach for this problem is the usage of Bayesian statistics and Monte Carlo Markov Chains (MCMC). MCMC methods can be computationally expensive and have been proven to be too slow for our research aims, so we tackle this problem with deep learning techniques, specifically Convolutional Neural Networks (CNNs). We developed two neural networks, which reproduce the same outcomes as the MCMC methods. Both have been trained with synthetic data as well as real data from the MCMC methods. The results obtained show negligible differences with the results from the MCMC methods but with the advantage of computing times orders of magnitude smaller.
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Recent advancements in spectropolarimetric instrumentation, such as the new facilities at the GREGOR and DKIST telescopes, have generated vast amounts of data with each observation. This increase in data volume results in longer processing times, heightened demands on computational resources, and an expanded carbon footprint, complicating scientific development timelines. The numerical inversion codes used for data analysis, based on radiative transfer models, are inherently complex. Modern projects focused on the solar atmosphere and its magnetic field require additional assumptions, significantly increasing processing times for each pixel. To address this challenge, new methods are being developed, leveraging modern data processing algorithms from statistics and machine learning. We are testing a 1D convolutional neural network model inspired by the 1D parallel atmosphere model of radiative transfer to enhance spectropolarimetric inversions and achieve significant reductions in processing times, as demonstrated in previous studies. Our approach aims to integrate physical constraints into the learning process, allowing the model to not only replicate inversions but also gain insights into the underlying physics. The data for our project was synthesized using state-of-the-art codes for magnetohydrodynamics (MURaM) and radiative transfer under non-local thermodynamic equilibrium (NICOLE). Preliminary results without physical constraints show loss rates approaching 10-3 order of magnitude and Pearson correlations of the order of 0.8 on average along different optical depths inverted in the process for thermodynamic quantities.
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Small solar flares, or microflares (GOES B class and fainter), are frequent bursts of energy released in the Sun’s atmosphere, exhibiting heating and particle acceleration similar to that of large flares. X-ray observations provide direct diagnostics to study these processes by examining thermal emission from the hot flare loops and non-thermal emission from accelerated electrons. We present analysis of the X-ray emission from small solar flares jointly observed with the Nuclear Spectroscopic Telescope Array (NuSTAR) and the Spectrometer Telescope for Imaging X-rays (STIX) on Solar Orbiter, providing different viewing angles of each event. NuSTAR is a highly sensitive X-ray imaging spectrometer that can directly image the Sun from 2.5 keV using focusing optics. STIX is an imaging spectrometer which instead uses indirect optics but has detectors capable of handling a wide range of solar X-ray fluxes from 4 to 150 keV. NuSTAR is in Earth orbit, whereas STIX is orbiting the Sun, so the two instruments combined can give different viewing angles of solar X-ray emission, providing a clearer picture of the flare’s structure. Combining analysis of NuSTAR and STIX’s X-ray spectra, we can take advantage of their different strengths, gaining a better understanding of the energy release in solar flares. We present observations of flares from June 2020 (on-disk for both instruments) and September 2022 (occulted for NuSTAR but on-disk for STIX - allowing us to probe a pre-flare non-thermal coronal source with NuSTAR and bright lower atmosphere flare emission with STIX).
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Magnetic reconnection and turbulence are two phenomena that are often invoked to address outstanding open questions as the energy dissipation problem and the heating and acceleration of the solar wind. These two phenomena are closely related to each other in a wide range of plasmas. Turbulent fluctuations can emerge in critical regions of reconnection events, and magnetic reconnection can occur as a product of the turbulent cascade. In this seminar I present some results exploring the interlink between turbulence and reconnection. This talk is divided in two sections, the first one Exploring the Effect of Driving Turbulent-like Fluctuations on a Harris Current Sheet Configuration and the Formation of Plasmoids and the second one Characterising Sub-Grid-Scale Effects on the Ohms Law Terms in Hybrid Simulations of Turbulence at the Earth’s Magnetosheath. The connecting thread is the non-linear and multi-scale nature of turbulence and reconnection as well as the importance of the small-scale dynamics on the large-scale one. In the first study, we perform 2D particle-in-cell simulations of a reconnecting Harris current sheet in the presence of turbulent fluctuations to explore the effect of turbulence on the reconnection process in collisionless non-relativistic pair-plasmas. We find that the presence of a turbulent field can affect the onset and evolution of magnetic reconnection. Moreover, we observe the existence of a scale dependent amplitude of magnetic field fluctuations above which these fluctuations can disrupt the growing of magnetic islands. These fluctuations provide thermal energy to the particles within the current sheet and preferential perpendicular thermal energy to the background population. In our second study we address the challenge that poses the modelling of large-scale systems while accounting for the small-scale phenomena by characterising the contribution of the small-scale dynamic terms on the generalized Ohms law in Vlasov-Hybrid simulations of turbulence in Earth’s magnetosheath. This with the aim of providing insight on Sub-Grid-Scale models that can be incorporated in Large Eddy Simulations. Our results are highly relevant to the future modelling of large-scale turbulent plasmas such as magnetospheres, the solar wind, the solar atmosphere, and other astrophysical systems.
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TBA
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