arXiv subjects

Alin Razvan Paraschiv

Publications and source records attributed to Alin Razvan Paraschiv.

6 recordsLinked to original sources

arXiv2026

Inferring 3D Coronal Magnetic Fields through Seismology-assisted Inversions of $IQU$-only Spectropolarimetric Observations

The routine measurement of the Stokes $IQUV$ signals of emission lines in the solar corona is still a challenging endeavor, particularly for observations using small-aperture instruments. Therefore, recent studies have explored the use of coronal seismology and propagating Alfvénic waves as alternative diagnostics of the coronal magnetic field. In particular, Z. Yang et al. showed that plane-of-sky (POS) phase-speed measurements provide a direct and consistent diagnostic of the POS magnetic-field component (B$_{\text{POS}}$). Building on recent theoretical advances in solar coronal polarization, we devise a novel inversion scheme and extended the CLEDB software package to exploit Stokes $IQU$ observations of two coronal emission lines combined with coronal seismology-derived B$_{\text{POS}}$ estimations. Using this framework, we perform a comprehensive statistical analysis demonstrating that this combination of diagnostics allows us to infer both magnetic-field orientation and strength with an accuracy comparable to that of Stokes $IQUV$ spectropolarimetry. This type of diagnostics is particularly suited for the Fe xiii 1074.7 and 1079.8 nm line pair routinely observed by new-generation instruments such as DKIST Cryo-NIRSP, DL-NIRSP, MLSO CoMP/UCoMP, and targeted by the future COSMO and CORSAIR efforts.

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arXiv2022

A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.

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arXiv2022

Predicting the Geoeffectiveness of CMEs Using Machine Learning

Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, having the potential to cause disturbances to telecommunication, satellite network disruptions, power grid damages and failures. Thus, considering these storms' potential effects on human activities, accurate forecasts of the geoeffectiveness of CMEs are paramount. This work focuses on experimenting with different machine learning methods trained on white-light coronagraph datasets of close to sun CMEs, to estimate whether such a newly erupting ejection has the potential to induce geomagnetic activity. We developed binary classification models using logistic regression, K-Nearest Neighbors, Support Vector Machines, feed forward artificial neural networks, as well as ensemble models. At this time, we limited our forecast to exclusively use solar onset parameters, to ensure extended warning times. We discuss the main challenges of this task, namely the extreme imbalance between the number of geoeffective and ineffective events in our dataset, along with their numerous similarities and the limited number of available variables. We show that even in such conditions, adequate hit rates can be achieved with these models.

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arXiv2022

Efficient and Automated Inversions of Magnetically-Sensitive Forbidden Coronal Lines: CLEDB -- The Coronal Line Emission DataBase Magnetic Field Inversion Algorithm

We present CLEDB, a "single point inversion" algorithm for inferring magnetic parameters using I,Q,U, and V Stokes parameters of forbidden magnetic dipole lines formed in the solar corona. We select lines of interest and construct databases of Stokes parameters for combinations of plasma thermal and magnetic configurations. The size and complexity of such databases are drastically reduced by taking advantage of symmetries. Using wavelength-integrated line profiles, each of which might be decomposed beforehand into several line-of-sight components, we search for nearest matches to observed Stokes parameters computed for the elongation corresponding to the observed region. The method is intended to be applied to two or more lines observed simultaneously. The solutions initially yield magnetic orientation, thermal properties, and the spatial position of the emitting plasma in three dimensions. Multiple possible solutions for each observation are returned, including irreducible degeneracies, where usually sets of two solutions are compatible with the two input I,Q,U, and V measurements. In solving for the scattering geometry, this method avoids an additional degeneracy pointed out by Dima & Schad (2020). The magnetic field strength is separately derived from the simple ratio of observed to database Stokes V data, after the thermal properties and scattering geometry solutions have been determined.

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arXiv2020

The Trigger Mechanism of Recurrent Solar Active Region Jets Revealed by the Magnetic Properties of a Coronal Geyser Site

Solar active region jets are small-scale collimated plasma eruptions that are triggered from magnetic sites embedded in sunspot penumbral regions. Multiple trigger mechanisms for recurrent jets are under debate. Vector magnetic field data from SDO-HMI observations are used to analyze a prolific photospheric configuration, identified in extreme ultraviolet observations as a `Coronal Geyser', that triggered a set of at least 10 recurrent solar active region jets. We focus on interpreting the magnetic fields of small-scale flaring sites aiming to understand the processes that govern recurrent jet eruptions. We perform a custom reprocessing of the SDO-HMI products, including disambiguation and uncertainty estimation. We scrutinized the configuration and dynamics of the photospheric magnetic structures. The magnetic configuration is described via the analysis of the photospheric magnetic vertical fields, to identify the process is responsible for driving the jet eruptions. We report that the two widely debated magnetic trigger processes, namely magnetic flux cancellation and magnetic flux emergence, appear to be responsible on a case by case basis for generating each eruption in our set. We find that 4 of 10 jets were due to flux cancellation while the rest were clearly not, and were more likely due to flux emergence.

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arXiv2019

On Solar Recurrent Coronal Jets: Coronal Geysers as Sources of Electron Beams and Interplanetary Type-III Radio Bursts

Coronal Jets are transitory small-scale eruptions omnipresent in solar observations. Active regions jets produce significant perturbations on the ambient solar atmosphere and are believed to be generated by microflare reconnection. Multiple sets of recurrent jets are identified in extreme-ultraviolet filter imaging. In this work we analyze the long time-scale recurrence of coronal jets originating from a unique footpoint structure observed in the lower corona. We report the detection of penumbral magnetic structures in the lower corona. These structures, that we entitled "Coronal Geysers", persist through multiple reconnection events that trigger recurrent jets in a quasi-periodical trend. Recurrent jet eruptions have been associated with Type-III radio bursts that are manifestations of traveling non-thermal electron beams.We examine the assumed link, as the coronal sources of interplanetary Type-III bursts are still open for debate. We scrutinized the hypothesized association by temporally correlating a statistically significant sample of six Geyser structures, that released at least 50 recurrent jets, with correspondent Type-III radio bursts detected in the interplanetary medium. Data analysis of these phenomena provides new information towards understanding of small-scale reconnection, non-thermal electron beam acceleration, and energy release. We find that the penumbral Geyser-like flaring structures produce recurring jets. They can be long-lived, quasi-stable, and act as coronal sources for Type-III bursts and implicitly for upwards accelerated electron beams.

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