3D solar coronal loop reconstructions with machine learning

The magnetic field plays an essential role in the initiation and evolution of different solar phenomena in the corona. The structure and evolution of the 3D coronal magnetic field are still not very well known. A way to get the 3D structure of the coronal magnetic field is by performing magnetic field extrapolations from the photosphere to the corona. In previous work, it was shown that by prescribing the 3D reconstructed loops' geometry, the magnetic field extrapolation finds a solution with a better agreement between the modeled field and the reconstructed loops. Also, it improves the quality of the field extrapolation. Stereoscopy represents the classical method for performing 3D coronal loop reconstruction. It uses at least two view directions. When only one vantage point of the coronal loops is available, other 3D reconstruction methods must be applied. Within this work, we present a method for the 3D loop reconstruction based on machine learning. Our purpose for developing this method is to use as many observed coronal loops in space and time for the modeling of the coronal magnetic field. Our results show that we can build machine learning models that can retrieve 3D loops based only on their projection information. In the end, the neural network model will be able to use only 2D information of the coronal loops, identified, traced and extracted from the EUV images, for the calculation of their 3D geometry.Comment: 7 Pages, 3 Figures, Accepted for publication on Astrophysical Journal Letter

Total electron content PCA-NN model for middle latitudes

A regression-based model was previously developed to forecast the total electron content (TEC) at middle latitudes. We present a more sophisticated model using neural networks (NN) instead of linear regression. This regional model prototype simulates and forecasts TEC variations in relation to space weather conditions. The development of a prototype consisted of the selection of the best set of predictors, NN architecture and the length of the input series. Tests made using the data from December 2014 to June 2018 show that the PCA-NN model based on a simple feed-forward NN with a very limited number (up to 6) of space weather predictors performs better than the PCA-MRM model that uses up to 27 space weather predictors. The prototype is developed on a TEC series obtained from a GNSS receiver at Lisbon airport and tested on TEC series from three other locations at middle altitudes of the Eastern North Atlantic. Conclusions on the dependence of the forecast quality on longitude and latitude are made.Comment: arXiv admin note: text overlap with arXiv:2201.0347

Mass-luminosity relation for FGK main sequence stars: metallicity and age contributions

The stellar mass-luminosity relation (MLR) is one of the most famous empirical "laws", discovered in the beginning of the 20th century. MLR is still used to estimate stellar masses for nearby stars, particularly for those that are not binary systems, hence the mass cannot be derived directly from the observations. It's well known that the MLR has a statistical dispersion which cannot be explained exclusively due to the observational errors in luminosity (or mass). It is an intrinsic dispersion caused by the differences in age and chemical composition from star to star. In this work we discuss the impact of age and metallicity on the MLR. Using the recent data on mass, luminosity, metallicity, and age for 26 FGK stars (all members of binary systems, with observational mass-errors <= 3%), including the Sun, we derive the MLR taking into account, separately, mass-luminosity, mass-luminosity-metallicity, and mass-luminosity-metallicity-age. Our results show that the inclusion of age and metallicity in the MLR, for FGK stars, improves the individual mass estimation by 5% to 15%.Comment: 7 pages, 4 figures, 1 table, accepted in Astrophysics and Space Scienc

The Automatic Identification and Tracking of Coronal Flux Ropes -- Part II: New Mathematical Morphology-based Flux Rope Extraction Method and Deflection Analysis

We present a magnetic flux rope (FR) extraction tool for solar coronal magnetic field modelling data, which builds upon the methodology from Wagner et al. (2023). We apply the scheme to magnetic field simulations of active regions AR12473 and AR11176. We compare the method to its predecessor and study the 3D movement of the newly extracted FRs up to heights of 200 and 300 Mm, respectively. The extraction method is based on the twist parameter and a variety of mathematical morphology algorithms, including the opening transform and the morphological gradient. We highlight the differences between the methods by investigating the circularity of the FRs in the plane we extract from. The simulations for the active regions are carried out with a time-dependent data-driven magnetofrictional model (TMFM; Pomoell et al. (2019)). We investigate the FR trajectories by tracking their apex throughout the full simulation time span. We demonstrate that this upgraded methodology provides the user with more tools and less a-priori assumptions about the FR shape that, in turn, leads to a more accurate set of field lines. The propagation analysis yields that the erupting FR from AR12473 showcases stronger dynamics than the AR11176 FR and a significant deflection during its ascent through the domain. The AR11176 FR appears more stable, though there still is a notable deflection. This confirms that at these low coronal heights, FRs do undergo significant changes in the direction of their propagation even for less dynamic cases. The modelling results are also verified with observations, with AR12473 being indeed dynamic and eruptive, while AR11176 only features an eruption outside of our simulation time window.Comment: Accepted for publication in Astronomy & Astrophysic

The Roles of the Sun Over the Last Centuries

Ricardo Jorge Gafeira, ‘The Roles of the Sun Over the Last Centuries’, talk presented at the symposium From Solar Futures to Future Solidarity, ICI Berlin, 23 October 2023, video recording, mp4, 16:58 <https://doi.org/10.25620/e231023_05

Solar filaments: characteristics and evolution

Este trabalho tem como principal objectivo o estudo dos flamentos solares, começando com um estudo estatístico que levou à criação e implementação de um modelo computacional. Os estudos estatísticos realizados estão relacionados com a longitude e latitude de Carrington, com o comprimento, a curvatura e a orientação dos filamentos. Durante a realização do estudo estatístico referente à orientação levantou-se a necessidade da elaboração do simulador. O simulador permitiu-nos verifcar que as alterações na distribuição da orientação observada eram causadas por efeitos de perspectiva. Outro elemento de interesse que podemos extrair do simulador é a distribuição da orientação de aparecimento dos filamentos, distribuição esta que até à data, tanto quanto pude verificar na bibliografia, não foi alvo de publicação

Revisiting the mass- and radius-luminosity relations for FGK main-sequence stars

Scaling relations are very useful tools for estimating unknown stellar quantities. Within this framework, eclipsing binaries are ideal for this goal because their mass and radius are known with a very good level of accuracy, leading to improved constraints on the models. We aim to provide empirical relations for the mass and radius as function of luminosity, metallicity, and age. We investigate, in particular, the impact of metallicity and age on those relations. We used a multi-dimensional fit approach based on the data from DEBCat, an updated catalogue of eclipsing binary observations such as mass, radius, luminosity, effective temperature, gravity, and metallicity. We used the {PARAM web interface for the Bayesian estimation of stellar parameters, along with} the stellar evolutionary code MESA to estimate the binary age, assuming a coeval hypothesis for both members. We derived the mass and radius-luminosity-metallicity-age relations using 56 stars, {with metallicity and mass in the range -0.34<[Fe/H]<0.27 and 0.66<M/M{_\odot}<1.8}. With that, the observed mass and radius are reproduced with an accuracy of 3.5% and 5.9%, respectively, which is consistent with the other results in literature. We conclude that including the age in such relations increases the quality of the fit, particularly in terms of the mass, as compared to the radius. On the other hand, as other authors have noted, we observed an higher dispersion on the mass relation than in that of the radius. We propose that this is due to a stellar age effect.Comment: 8 pages, 9 figures, Accepted for publication on Astronomy and Astrophysic

Total Electron Content PCA-NN Prediction Model for South-European Middle Latitudes

A regression-based model was previously developed to forecast total electron content (TEC) at middle latitudes. We present a more sophisticated model using neural networks (NN) instead of linear regression. This regional model prototype simulates and forecasts TEC variations in relation to space weather conditions. The development of a prototype consisted of the selection of the best set of predictors, NN architecture, and the length of the input series. Tests made using the data from December 2014 to June 2018 show that the PCA-NN model based on a simple feed-forward NN with a very limited number (up to six) of space weather predictors performs better than the PCA-MRM model that uses up to 27 space weather predictors. The prototype is developed on a TEC series obtained from a GNSS receiver at Lisbon airport and tested on TEC series from three other locations at middle latitudes of the Eastern North Atlantic. Conclusions on the dependence of the forecast quality on longitude and latitude are made

Machine learning in solar physics

Abstract The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field