arXiv subjects

Daria Sorokina

Publications and source records attributed to Daria Sorokina.

5 recordsLinked to original sources

arXiv2026

Streamer slab eigenmode analysis with the Legolas code

Context. Helmet streamers are large ray-like structures extending from the solar corona that thin further away from the solar surface, forming an extended current sheet. These structures are quasi-stable, and are observed to support kink waves travelling outward from the Sun, called helmet streamer waves. Aims. Limited analytical models identify streamer waves as fast body kink eigenmodes of the streamer slab. To bridge the gap between analytical models and numerical simulations, we investigate the eigenmode spectrum of more realistic streamer slab configurations, obtained from simulations, to retrieve the kink profiles and firmly establish that streamer waves are eigenmodes of the streamer. Methods. Using a streamer slab model extracted from numerical simulations, the Legolas code is applied to compute the eigenmode spectrum, for wavelengths observed in the simulation. In the spectrum we identify the mode matching the behaviour in the non-linear simulations by comparing to analytical and numerical results for the established Epstein profile. The identified modes's phase speeds are then compared to analytical, simulation, and observational results. Results. For each case, the spectrum is found to contain a mode matching the properties of a streamer wave. The phase speeds of the identified modes are compatible with those measured in observations, strongly suggesting that the observed streamer waves are fast body kink modes. Comparison to the analytical slab model is difficult due to the sensitivity of the analytical dispersion relation to the internal values.

astro-ph.SR↗

arXiv2026

Time-evolving coronal modelling of the solar maximum around the solar storms in May 2024 by COCONUT

Time-evolving MHD coronal models deliver more realistic results than traditional quasi-steady-state models. The fully implicit time-evolving coronal model COCONUT performs efficiently enough for real-time coronal simulations during solar minimum. However, during solar maxima, the coronal magnetic field is more complex and stronger, and coronal structures evolve more rapidly than during solar minima. Time-evolving MHD coronal modelling of solar maxima often struggles with poor numerical stability and low computational efficiency. We enhanced the numerical stability of the time-evolving coronal model COCONUT to mitigate these issues with the aim to evaluate the differences between the time-evolving and quasi-steady-state coronal simulation results, and to assess the impact of the spatial resolution on global MHD coronal modelling of solar maxima. After enhancing the positivity-preserving property of COCONUT, we employed it to simulate the evolution of coronal structures within 0.1 AU in an inertial coordinate system over two CRs around the solar storms in May 2024. These simulations were performed on unstructured geodesic meshes containing 6.06, 1.52, and 0.38 M cells. We also conducted a quasi-steady-state coronal simulation that treated the solar surface as a rigidly rotating spherical shell. A comparison with observations further validated the reliability of the time-evolving coronal modelling technique. It shows that incorporating the evolution of the magnetic field on the solar surface can significantly improve the fidelity of global MHD coronal simulations around a solar maximum. A simulated magnetic field strength using a mesh with 6.06 M cells can be stronger by more than 40% than that in a mesh with 0.38 M cells. The fully implicit time-evolving model COCONUT shows promise for accurately conducting real-time global coronal simulations of solar maxima.

astro-ph.SR↗

arXiv2025

Total solar eclipse 2024 modelling with COCONUT

Coronal modelling is crucial for a better understanding of solar and helio-physics. Due to the strong brightness of the Sun and the lack of white light observations of the solar atmosphere and low corona (1-1.5R$_\odot$), total solar eclipses have become a standard approach for validating the coronal models. In this study, we validate the COCONUT coronal model by predicting the coronal configuration during the total solar eclipse on April 8, 2024. We aim to predict the accurate configuration of the solar corona during the total solar eclipse on April 8, 2024. We utilise the full 3D MHD model to reconstruct the solar corona from the solar surface to $30\;R_\odot$. The upcoming total solar eclipse predictions were conducted in three different regimes: quasi-steady driving of the inner boundary conditions (BCs) with a daily cadence and dynamic driving of the inner BCs with both daily and hourly cadences. The results from all the simulations are compared to the total solar eclipse images. Additionally, the synthetic white-light (WL) images are generated from the STEREO-A field of view and compared to COR2 observed images. Normalised polarised brightness is compared in the COR2 and synthetic WL images. The predicted solar corona does not vary significantly in the first half of the prediction window. The dynamic simulations yielded better results than the quasi-steady predictions. The west limb was reconstructed better in the simulations than the east limb. We have predicted the total solar eclipse coronal configuration 18 days before the total solar eclipse. We can conclude that the dynamic simulations produced more accurate predictions. The availability of comprehensive observations is crucial, as the emergence of the active region on the east limb made it difficult to accurately predict the east limb coronal configuration due to incorrect input of magnetic field data.

astro-ph.SR↗

arXiv2021

Scalable Feature Selection for (Multitask) Gradient Boosted Trees

Gradient Boosted Decision Trees (GBDTs) are widely used for building ranking and relevance models in search and recommendation. Considerations such as latency and interpretability dictate the use of as few features as possible to train these models. Feature selection in GBDT models typically involves heuristically ranking the features by importance and selecting the top few, or by performing a full backward feature elimination routine. On-the-fly feature selection methods proposed previously scale suboptimally with the number of features, which can be daunting in high dimensional settings. We develop a scalable forward feature selection variant for GBDT, via a novel group testing procedure that works well in high dimensions, and enjoys favorable theoretical performance and computational guarantees. We show via extensive experiments on both public and proprietary datasets that the proposed method offers significant speedups in training time, while being as competitive as existing GBDT methods in terms of model performance metrics. We also extend the method to the multitask setting, allowing the practitioner to select common features across tasks, as well as selecting task-specific features.

stat.ML↗

arXiv2007

Plagiarism Detection in arXiv

We describe a large-scale application of methods for finding plagiarism in research document collections. The methods are applied to a collection of 284,834 documents collected by arXiv.org over a 14 year period, covering a few different research disciplines. The methodology efficiently detects a variety of problematic author behaviors, and heuristics are developed to reduce the number of false positives. The methods are also efficient enough to implement as a real-time submission screen for a collection many times larger.

cs.DB↗