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Kamen Kozarev

Publications and source records attributed to Kamen Kozarev.

17 recordsLinked to original sources

The SKA as a Probe of Heliospheric Turbulence and Radio-wave Propagation Effects

Density turbulence in the heliosphere can impact traversing radio photons originating from anywhere in the universe, leading to distortions of both the spectroscopic and imaging properties of the radio sources. Extra-solar radio sources exhibit scintillation and angular broadening, while monochromatic signals from spacecraft are spectrally broadened. It was recently demonstrated that such impacts, referred to as radio-wave propagation effects, are particularly significant in observations of solar radio bursts excited through the plasma emission mechanism. A comparison of detailed observations with simulations is required in order to quantify the radio-wave propagation effects and disentangle the true radio-source properties from the observed ones, enabling the diagnosis of the heliospheric environment. Consequently, theoretical advancements and our ability to quantify the heliospheric turbulence depend on the quality and quantity of available observations. SKA pathfinders, like LOFAR, have been key to the significant progress recently achieved, but have also highlighted areas where the available observing capabilities are lacking. The SKA's unrivalled sensitivity will be crucial in identifying and distinguishing fine and faint radio structures, where our ability to model them defines whether we can accurately describe the heliospheric turbulence and deduce the fundamental properties of the radio sources. The SKA will be an indispensable tool in observing solar radio emissions from the Sun to beyond 1 au, complemented by ground-based interferometers that can reach frequencies down to the ionospheric cut-off at 10 MHz and space-based radio instruments which cover frequencies down to a few kHz.

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Long-Baseline VLF Observations of Solar Flares from Antarctica

We present long-baseline Very Low Frequency (VLF) observations of solar flare-induced ionospheric disturbances obtained at the Bulgarian Polar Astronomical Observatory (St. Kliment Ohridski Base) on Livingston island, Antarctica. Using continuous VLF transmissions at 21.4 kHz (NPM, Hawaii) and 24.0 kHz (NAA, Maine), propagating over trans-hemispheric paths exceeding 11000 km, we analyze observations of solar flares during the period 24 January--8 February 2025. After removing the strong diurnal signal via superposed epoch analysis, we analyse the flare-related perturbations in VLF amplitude and their correlation with GOES soft X-ray flux for 250 flares of C and M class. The long propagation paths provide enhanced sensitivity to flare-driven changes in D-region ionization. The observations reveal clear, frequency-dependent responses and measurable time delays between X-ray and VLF peaks. These delays, including cases of near-zero or negative lag for stronger events, highlight the role of flare spectral characteristics and D-region recombination processes. Our results demonstrate the scientific value of long-baseline Antarctic VLF observations for detecting and timing GOES-class flares, while also highlighting key limitations, such as background variability and path-dependent propagation effects, which must be quantified for reliable VLF-based flare monitoring.

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Role of SKA in Advancing Remote Measurements of Magnetic Fields of Solar Coronal Mass Ejections

Coronal Mass Ejections (CMEs) are large expulsions of magnetized plasma from the Sun into interplanetary space and are the primary drivers of extreme space weather variations. The strength and topology of CME magnetic fields largely determine their impact on Earth. Although visible-light coronagraphs routinely observe CMEs and provide their geometric and kinematic properties, they cannot directly measure CME vector magnetic fields. These fields evolve from initiation through the inner heliosphere due to interactions with other CMEs, coronal structures, and the ambient solar wind, leading to significant structural deformation. Such evolution complicates predictions of the CME magnetic field at Earth. Accurate measurements of CME magnetic fields in the corona and heliosphere are therefore essential for advancing space weather forecasting. Radio observations spanning MHz to GHz frequencies provide a powerful remote-sensing approach for measuring CME magnetic fields from the ground. Recent observations with Square Kilometre Array (SKA) precursors and pathfinder instruments, as well as other new-generation facilities, have demonstrated the potential of these radio techniques for CME magnetic-field diagnostics. At the same time, these studies have highlighted several limitations of current instruments. The higher sensitivity, wider instantaneous bandwidth, and broader frequency coverage of the SKA will open a new observational window, enabling these techniques to be fully exploited for constraining SpWx models and improving predictive accuracy. However, such observations are non-standard and require special consideration in scheduling, calibration, and imaging. Developments achieved with SKA precursors and pathfinders are paving the way for robust CME magnetic-field measurements with the SKA.

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Review of Machine Learning Models for Solar Energetic Particle Prediction

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

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Segmentation and Tracking of Eruptive Solar Phenomena with Convolutional Neural Networks

Solar eruptive events are complex phenomena, which most often include coronal mass ejections (CME), CME-driven compressive and shock waves, flares, and filament eruptions. CMEs are large eruptions of magnetized plasma from the Sun's outer atmosphere or corona, that propagate outward into the interplanetary space. Over the last several decades a large amount of remote solar eruption observational data has become available from ground-based and space-borne instruments. This has recently required the development of software approaches for automated characterisation of eruptive features. Most solar feature detection and tracking algorithms currently in use have restricted applicability and complicated processing chains, while complexity in engineering machine learning (ML) training sets limit the use of data-driven approaches for tracking or solar eruptive related phenomena. Recently, we introduced Wavetrack - a general algorithmic method for smart characterization and tracking of solar eruptive features. The method, based on a-trous wavelet decomposition, intensity rankings and a set of filtering techniques, allows to simplify and automate image processing and feature tracking. Previously, we applied the method successfully to several types of remote solar observations. Here we present the natural evolution of this approach. We discuss various aspects of applying Machine Learning (ML) techniques towards segmentation of high-dynamic range heliophysics observations. We trained Convolutional Neural Network (CNN) image segmentation models using feature masks obtained from the Wavetrack code. We present results from pre-trained models for segmentation of solar eruptive features and demonstrate their performance on a set of CME events based on SDO/AIA instrument data.

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New Bulgarian-Austrian project 'Joint observations and investigations of solar chromospheric and coronal activity'

We present the bilateral collaboration between Bulgarian and Austrian solar and space weather researchers on the topic of chromospheric and coronal activity. This new project will focus, on one hand, on the technical setup and calibration of the new Rozhen chromospheric telescope at the National Astronomical Observatory (NAO) by means of establishing optimal observational programs for different quiet-Sun and activity phenomena, automating the data collection and storage, implementing machine/deep learning models for feature recognition. The second aim is to carry out joint scientific analyses of solar phenomena using observations from ground-based instruments in both countries, and supplementary spacecraft data. The successful implementation of solar monitoring at NAO-Rozhen will facilitate the overall visibility of the Bulgarian instrument and generate interest towards astronomy and solar physics not only for PhD students and young scientists but also for the general public.

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A network for solar optical observations in Bulgaria

The report presents a new initiative for the development of a network of ground-based stations for solar observations in the optical range. Three separate locations in Bulgaria have installed instrumentation for solar dedicated observations. The currently used telescopes (type, mounting, guiding systems) and designated filters (white-light, H-alpha) are described in detail. Test images are also included. Future plans for improvements are briefly discussed.

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Multi-Instrument Observations and Tracking of a Coronal Mass Ejection Front From Low to Middle Corona

The shape and dynamics of coronal mass ejections (CMEs) vary significantly based on the instrument and wavelength used. This has led to significant debate about the proper definitions of CME/shock fronts, pile-up/compression regions, and cores observations in projection in optically thin vs. optically thick emission. Here we present an observational analysis of the evolving shape and kinematics of a large-scale CME that occurred on May 7, 2021 on the eastern limb of the Sun as seen from 1 au. The eruption was observed continuously, consecutively by the Atmospheric Imaging Assembly (AIA) telescope suite on the Solar Dynamics Observatory (SDO), the ground-based COronal Solar Magnetism Observatory (COSMO) K-coronagraph (K-Cor) on Mauna Loa, and the C2 and C3 telescopes of the Large Angle Solar Coronagraph (LASCO) on the Solar and Heliospheric Observatory (SoHO). We apply the updated multi-instument version of the recently developed Wavetrack Python suite for automated detection and tracking of coronal eruptive features to evaluate and compare the evolving shape of the CME front as it propagated from the solar surface out to 20 solar radii. Our tool allows tracking features beyond just the leading edge and is an important step towards semi-automatic manufacturing of training sets for training data-driven image segmentation models for solar imaging. Our findings confirm the expected strong connection between EUV waves and CMEs. Our novel, detailed analysis sheds observational light on the details of EUV wave-shock-CME relations that is lacking for the gap region between the low and middle corona.

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Modeling Metric Gyrosynchrotron Radio Emission From the Quiet Solar Corona

The radio emission of the quiet Sun in the metric and decametric bands has not been well studied historically due to limitations of existing instruments. It is nominally dominated by thermal brehmsstrahlung of the solar corona, but may also include significant gyrosynchrotron emission, usually assumed to be weak under quiet conditions. In this work, we investigate the expected gyrosynchrotron contribution to solar radio emission in the lowest radio frequencies observable by ground instruments, for different regions of the low and middle corona. We approximate the coronal conditions by a synoptic magnetohydrodynamic (MHD) model. The thermal emission is estimated from a forward model based on the simulated corona. We calculate the expected gyrosynchrotron emission with the Fast Gyrosynchrotron Codes framework by Fleishman & Kuznetsov (2010). The model emissions of different coronal regions are compared with quiet-time observations between 20-90 MHz by the LOw Frequency ARray (LOFAR) radio telescope. The contribution of gyrosynchrotron radiation to low frequency solar radio emission may shed light on effects such as the hitherto unexplained brightness variation observed in decametric coronal hole emission, and help constrain measurements of the coronal magnetic fields. It can also improve our understanding of electron populations in the middle corona and their relation to the formation of the solar wind.

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Characterization of the Early Dynamics of Solar Coronal Bright Fronts

We present a comprehensive characterization of 26 CME-driven compressive waves known as Coronal Bright Fronts (CBFs) observed in the low solar corona between 2010 and 2017. These CBFs have been found to be associated with SEP events near Earth, indicating their importance in understanding space weather phenomena. The aim of this study is to analyze and describe the early dynamics of CBFs using a physics-based heliospheric SEP forecasting system known as the SPREAdFAST framework. This framework utilizes a chain of data-driven analytic and numerical models to predict SEP fluxes at multiple locations in the inner heliosphere by considering their acceleration at CMEs near the Sun and subsequent interplanetary transport. To estimate the time-dependent plasma and compression parameters of the CBFs, we utilized sequences of base-difference images obtained from the AIA instrument on board the SDO satellite, and measurements of the height-time profiles of the CMEs obtained from the LASCO instrument on board the SOHO satellite. We employed kinematic measurements and plasma model results to derive these parameters. The SPREAdFAST framework facilitated the analysis and correlation of these observations with SEP events near Earth. Our analysis yielded statistical relations and distributions for both the shocks and plasma parameters associated with the 26 CBFs investigated. By combining the observations from the AIA and LASCO instruments, as well as the data products from the SPREAdFAST framework, we obtained a comprehensive understanding of the early dynamics of CBFs, including their temporal evolution, plasma properties, and compressional characteristics. These findings contribute to the growing body of knowledge in the field and have implications for space weather forecasting and the study of SEP events.

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Coronal Diagnostics of Solar Type-III Radio Bursts Using LOFAR and PSP Observations

This study aims to investigate the ambiguous source and the underlying physical processes of the solar type III radio bursts that occurred on April 3, 2019, through the utilization of multiwavelength observations from the LOFAR radio telescope and the PSP space mission, as well as incorporating results from a PFSS and MHD models. The primary goal is to identify the spatial and temporal characteristics of the radio sources, as well as the plasma conditions along their trajectory. Data preprocessing techniques are applied to combine high- and low-frequency observations from LOFAR and PSP between 2.6 kHz and 80 MHz. We then extract information on the frequency drift and speed of the accelerated electron beams from the dynamic spectra. Additionally, we use LOFAR interferometric observations to image the sources of the radio emission at multiple frequencies and determine their locations and kinematics in the corona. Lastly, we analyze the plasma parameters and magnetic field along the trajectories of the radio sources using PFSS and MHD model results. We present several notable findings related to type III radio bursts. Firstly, through our automated implementation, we were able to effectively identify and characterize 9 type III radio bursts in the LOFAR-PSP combined dynamic spectrum and 16 type III bursts in the LOFAR dynamic spectrum. Secondly, our imaging observations show that the electrons responsible for these bursts originate from the same source and within a short time frame of fewer than 30 minutes. Finally, our analysis provides informative insights into the physical conditions along the path of the electron beams. For instance, we found that the plasma density obtained from the MAS model is significantly lower than the expected theoretical density.

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Forecasting Solar Energetic Proton Integral Fluxes with Bi-Directional Long Short-Term Memory Neural Networks

Solar energetic particles are mainly protons and originate from the Sun during solar flares or coronal shock waves. Forecasting the Solar Energetic Protons (SEP) flux is critical for several operational sectors, such as communication and navigation systems, space exploration missions, and aviation flights, as the hazardous radiation may endanger astronauts', aviation crew and passengers' health, the delicate electronic components of satellites, space stations, and ground power stations. Therefore, the prediction of the SEP flux is of high importance to our lives and may help mitigate the negative impacts of one of the serious space weather transient phenomena on the near-Earth space environment. Numerous SEP prediction models are being developed with a variety of approaches, such as empirical models, probabilistic models, physics-based models, and AI-based models. In this work, we use the bi-directional long short-term memory (BiLSTM) neural network model architecture to train SEP forecasting models for 3 standard integral GOES channels (>10 MeV, >30 MeV, and >60 MeV) with 3 forecast windows (1-day, 2-day, and 3-day ahead) based on daily data obtained from the OMNIWeb database from 1976 to 2019. As the SEP variability is modulated by the solar cycle, we select input parameters that capture the short-term, typically within a span of a few hours, and long-term, typically spanning several days, fluctuations in solar activity. We take the F10.7 index, the sunspot number, the time series of logarithm of the x-ray flux, the solar wind speed, and the average strength of the interplanetary magnetic field as input parameters to our model. The results are validated with an out-of-sample testing set and benchmarked with other types of models.

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RFI Flagging in Solar and Space Weather Low Frequency Radio Observations

Radio spectroscopy provides a unique inspection perspective for solar and space weather research, which can reveal the plasma and energetic electron information in the solar corona and inner heliosphere. However, Radio-Frequency Interference (RFI) from human activities affects sensitive radio telescopes, and significantly affects the quality of observation. Thus, RFI detection and mitigation for the observations is necessary to obtain high quality, science-ready data. The flagging of RFI is particularly challenging for the solar and space weather observations at low frequency, because the solar radio bursts can be brighter than the RFI, and may show similar temporal behavior. In this work, we investigate RFI flagging methods for solar and space weather observations, including a strategy for AOFlagger, and a novel method that makes use of a morphology convolution. These algorithms can effectively flag RFI while preserving solar radio bursts.

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Multi-Scale Image Preprocessing and Feature Tracking for Remote CME Characterization

Coronal Mass Ejections (CMEs) influence the interplanetary environment over vast distances in the solar system by injecting huge clouds of fast solar plasma and energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced, but current understanding points to CME-driven shocks and compressions in the solar corona. At the same time, unprecedented remote and in situ (Parker Solar Probe, Solar Orbiter) solar observations are becoming available to constrain existing theories. Here we present a general method for recognition and tracking on solar images of objects such as CME shock waves and filaments. The calculation scheme is based on a multi-scale data representation concept a trous wavelet transform, and a set of image filtering techniques. We showcase its performance on a small set of CME-related phenomena observed with the SDO/AIA telescope. With the data represented hierarchically on different decomposition and intensity levels, our method allows to extract certain objects and their masks from the imaging observations, in order to track their evolution in time. The method presented here is general and applicable to detecting and tracking various solar and heliospheric phenomena in imaging observations. It holds potential to prepare large training data sets for deep learning. We have implemented this method into a freely available Python library.

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Imaging of the Quiet Sun in the Frequency Range of 20-80 MHz

Radio emission of the quiet Sun is considered to be due to thermal bremsstrahlung emission of the hot solar atmosphere. The properties of the quiet Sun in the microwave band have been well studied, and they can be well described by the spectrum of bremsstrahlung emission. In the meter-wave and decameter-wave bands, properties of the quiet Sun have rarely been studied due to the instrumental limitations. In this work, we use the LOw Frequency ARray (LOFAR) telescope to perform high quality interferometric imaging spectroscopy observations of quiet Sun coronal emission at frequencies below 90~MHz. We present the brightness temperature spectrum, and size of the Sun in the frequency range of 20-80~MHz. We report on dark coronal regions with low brightness temperature that persist with frequency. The brightness temperature spectrum of the quiet Sun is discussed and compared with the bremsstrahlung emission of a coronal model and previous quiet Sun observations.

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A Multi-Event Study of Early-Stage SEP Acceleration by CME-Driven Shocks -- Sun to 1 AU

The solar corona below 10 solar radii is an important region for early acceleration and transport of solar energetic particles (SEPs) by coronal mass ejection-driven shock waves. There, these waves propagate into a highly variable dynamic medium with steep gradients and rapidly expanding coronal magnetic fields, which modulates the particle acceleration near the shock or wave surfaces, and the way SEPs spread into the heliosphere. We present a study modelling the acceleration of SEPs in global coronal shock events in the corona, as well as their transport to 1 au, based on telescopic observations coupled with dynamic physical models.

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Early-Stage SEP Acceleration by CME-Driven Shocks with Realistic Seed Spectra: I. Low corona

An outstanding problem in heliospheric physics is understanding the acceleration of solar energetic particles (SEP) in coronal mass ejections (CMEs) and flares. A fundamental question is whether the acceleration occurs in interplanetary space, or near the Sun. Recent work has shown that CME-driven shocks may produce SEPs while still below 5 solar radii. In this work we explore SEP acceleration during the onset of CMEs and shocks even lower in the corona, using realistic suprathermal spectra, for a selection of events. We have calculated quiet-time, pre-event suprathermal particle spectra from 1 AU observations, and scaled them back to the low corona to serve as seed spectra. For each event, AIA observations and the CASHeW framework were used to model the compressive/shock wave kinematics and its interaction with the corona. The proton acceleration was then modeled using an analytic diffusive shock acceleration model as the shock waves propagate between 1.05 and 1.3 solar radii. We demonstrate the capability of low coronal shock-related EUV waves to accelerate protons to multi-MeV energies in a matter of minutes, in the very early stages of the associated solar eruptions. We find that strong proton energization occurs for high values of the density jump, Alfven Mach number, and shock speed. In future work the results of this early-stage shock acceleration will be used to model the continued acceleration higher in the corona.

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