Solar and Stellar Astrophysics (astro-ph.SR)

Abstract: Solar eruptions are the leading driver of space weather, and it is vital for space weather forecast to understand in what conditions the solar eruptions can be produced and how they are initiated. The rotation of sunspots around their umbral center has long been considered as an important condition in causing solar eruptions. To unveil the underlying mechanisms, here we carried out a data-driven magnetohydrodynamics simulation for the event of a large sunspot with rotation for days in solar active region NOAA 12158 leading to a major eruption. The photospheric velocity as recovered from the time sequence of vector magnetograms are inputted directly at the bottom boundary of the numerical model as the driving flow. Our simulation successfully follows the long-term quasi-static evolution of the active region until the fast eruption, with magnetic field structure consistent with the observed coronal emission and onset time of simulated eruption matches rather well with the observations. Analysis of the process suggests that through the successive rotation of the sunspot the coronal magnetic field is sheared with a vertical current sheet created progressively, and once fast reconnection sets in at the current sheet, the eruption is instantly triggered, with a highly twisted flux rope originating from the eruption. This data-driven simulation stresses magnetic reconnection as the key mechanism in sunspot rotation leading to eruption.

Abstract: Several recent studies utilizing different helioseismic methods have confirmed the presence of large-scale vorticity waves known as solar Rossby waves within the Sun. Rossby waves are distinct from acoustic waves, typically with longer periods and lifetimes; and their general properties, even if only measured at the surface, may be used to infer properties of the deeper convection zone, such as the turbulent viscosity and entropy gradients which are otherwise difficult to observe. In this study, we utilize $12~$years of inverted subsurface velocity fields derived from the SDO/HMI's time--distance and ring-diagram pipelines to investigate the propoerty of the solar equatorial Rossby waves. By covering the maximum and the decline phases of Solar Cycle 24, these datasets enable a systematic analysis of any potential cycle dependence of these waves. Our analysis provides evidence of a correlation between the average power of equatorial Rossby waves and the solar cycle, with stronger Rossby waves during the solar maximum and weaker waves during the minimum. Our result also shows that the frequency of the Rossby waves is lower during the magnetic active years, implying a larger retrograde drift relative to the solar rotation. Although the underlying mechanism that enhances the Rossby wave power and lowers its frequency during the cycle maximum is not immediately known, this observation has the potential to provide new insights into the interaction of large-scale flows with the solar cycle.

Abstract: The third data release of Gaia has provided approximately 220 million low resolution spectra. Among these, about 100,000 correspond to white dwarfs. The magnitude of this quantity of data precludes the possibility of performing spectral analysis and type determination by human inspection. In order to tackle this issue, we explore the possibility of utilising a machine learning approach, based on a Random Forest algorithm. We aim to analyze the viability of the Random Forest algorithm for the spectral classification of the white dwarf population within 100 pc from the Sun, based on the Hermite coefficients of Gaia spectra. We utilized the assigned spectral type from the Montreal White Dwarf Database for training and testing our Random Forest algorithm. Once validated, our algorithm model is applied to the rest of unclassified white dwarfs within 100 pc. First, we started by classifying the two major spectral type groups of white dwarfs: hydrogen-rich (DA) and hydrogen-deficient (non-DA). Next, we explored the possibility of classifying the various spectral subtypes, including in some cases the secondary spectral types. Our Random Forest classification presented a very high recall (>80%) for DA and DB white dwarfs, and a very high precision (>90%) for DB, DQ and DZ white dwarfs. As a result we have assigned a spectral type to 9,446 previously unclassified white dwarfs: 4,739 DAs, 76 DBs (60 of them DBAs), 4,437 DCs, 132 DZs and 62 DQs (9 of them DQpec). Despite the low resolution of Gaia spectra, the Random Forest algorithm applied to the Gaia spectral coefficients proves to be a highly valuable tool for spectral classification.