arXiv subjects

Viacheslav M Sadykov

Publications and source records attributed to Viacheslav M Sadykov.

14 recordsLinked to original sources

arXiv2026

Solar Wind Heating Near the Sun: A Radial Evolution Approach

Characterizing the plasma state in the near-Sun environment is essential to constrain the mechanisms that heat and accelerate the solar wind. In this study, we use Parker Solar Probe (PSP) observations from Encounters 1 through 24 to investigate the radial evolution of solar wind plasma and magnetic field properties in this region. Using intervals with high field-of-view ($>85\%$) coverage, we derive the radial profiles of magnetic field strength ($|B|$), proton density ($N$), bulk speed ($V$), total proton temperature ($T$), parallel ($T_\parallel$) and perpendicular ($T_\perp$) temperatures, temperature anisotropy ($T_\perp/T_\parallel$), plasma beta ($β$), Alfvén Mach number ($M_A$), and magnetic field fluctuations ($δB/B$) for sub and super-Alfvénic regions. In super-Alfvénic regions, power-law of $|B|$, $N$, $V$, and $T$ as a function of heliocentric distance are broadly consistent with previous \textit{Helios} results at $>0.3$ AU. The radial evolution of the components of the temperature tensor reveals distinct behavior: $T_\perp$ decreases monotonically with distance, whereas $T_\parallel$ exhibits a non-monotonic trend -- decreasing in the sub-Alfvénic region, increasing just beyond the Alfvén surface. We interpret the increase in $T_\parallel$ as a proxy for proton beam occurrence. We further examine the evolution of magnetic field fluctuations, finding decreasing radial/parallel fluctuations but enhanced tangential/normal/perpendicular fluctuations in sunward direction. These fluctuations may provide free energy for beam generation and particle heating via wave-particle interactions.

astro-ph.SR↗

arXiv2026

Machine Learning-Ready Data Sets for the Analysis and Nowcasting of Atmospheric Radiation at Aviation Altitudes

Nowcasting and forecasting of the radiation environment in the Earth's lower atmosphere are critical for the safety of aircraft and spacecraft crews and passengers. Currently, this problem is addressed by employing statistical and physics-based models that take into account particle transport and precipitation. However, given the increased number of radiation measurements available to the community, it is possible to start developing data-driven approaches. We prepared Machine Learning-ready (ML-ready) datasets to nowcast the effective dose rates at aviation altitudes. The presented datasets contain 92,476 individual measurements from 589 flights obtained by the Automated Radiation Measurements for Aerospace Safety (ARMAS) experiment from 2013 to 2023. The ARMAS measurements are augmented with the properties of the Geospace environment, such as solar soft X-ray and proton fluxes, solar wind properties, secondary cosmic ray neutrons, space weather indexes, and global solar activity indicators (such as daily sunspot number). ARMAS data are separated into three partitions, ensuring that (1) the data points from a single flight remain within the same partition, and (2) each partition samples the flight locations and Geospace environment conditions equally. Several versions of the datasets allow predictions based on point-in-time measurements and use up to 24 hours of Geospace parameter history. The test of the use case demonstrates a possibility of nowcasting ARMAS measurements with accuracies slightly better than the considered physics-based models. The publicly available ML-ready datasets could serve as the first step in data preparation for ML-driven nowcasting and forecasting of the radiation environment.

physics.ao-ph↗

arXiv2025

Nowcasting of Aviation Radiation Using Geospace Environment Properties: A Machine Learning Approach

Radiation exposure at aviation altitudes presents significant health risks to aircrews due to the cumulative effects of ionizing radiation. Physics-based models estimate radiation levels based on geophysical and atmospheric parameters, but often struggle to capture the highly dynamic and complex nature of the radiation environment, limiting their real-time predictive capabilities. To address this challenge, we investigate machine learning (ML) methods to enhance real-time radiation nowcasting. Leveraging newly compiled ML-ready datasets, publicly available at https://dmlab.cs.gsu.edu/rdp/, we train supervised models capable of capturing both linear and non-linear relationships between Geospace conditions and atmospheric radiation levels. Our experiments demonstrate that the XGBoost model achieves approximately 10 percent improvement in prediction accuracy over the considered physics-based model. Furthermore, feature importance analysis reveals that certain Geospace properties, specifically solar polar fields, solar wind properties, and neutron monitor data, are impacting the nowcast of the radiation levels at flight altitudes. These findings suggest meaningful physical relationships between the near-Earth space environment and atmospheric radiation, and highlight the potential of ML-based approaches for operational space weather applications.

physics.space-ph↗

arXiv2025

Identification of Ion-Kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning

Analysis of ion-kinetic instabilities in solar wind plasmas is crucial for understanding energetics and dynamics throughout the heliosphere, as evident from spacecraft observations of complex ion velocity distribution functions (VDFs) and ubiquitous ion-scale kinetic waves. In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. Using 34 hybrid particle-in-cell simulations of kinetic protons and $α$-particles initialized using plasma parameters derived from solar wind observations, we prepare a dataset of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. We compare feature-based classifiers applied to VDF moments, such as Support Vector Machine and Random Forest, with DL convolutional neural networks (CNN) applied directly to VDFs as images in the gyrotropic velocity plane. The best-performing classifier, Random Forest, has an accuracy of $0.96\pm0.01$, and a true skill score of $0.89\pm0.03$, with the majority of missed predictions made near stability thresholds. We study how the variations of the temporal derivative thresholds of anisotropies and magnetic energies and sampling strategies for simulation runs affect classification. CNN-based models have the highest accuracy of $0.88\pm0.18$ among all considered if evaluated on the runs entirely not used during the model training. The addition of the $E_{\perp}$ power spectrum as an input for the ML models leads to the improvement of instability analysis for some cases. The results demonstrate the potential of ML and DL for the detection of ion-scale kinetic instabilities using spacecraft observations of solar wind and magnetospheric plasmas.

astro-ph.SR↗

arXiv2025

Evidence of Interaction between Ion-Scale Waves and Ion Velocity Distributions in the Solar Wind

Recent in situ observations from Parker Solar Probe (PSP) near perihelia reveal ion beams, temperature anisotropies, and kinetic wave activity. These features are likely linked to solar wind heating and acceleration. During PSP Encounter 17 (at $11.4R_s$) on Sep-26-2023, the PSP/FIELDS instrument detected enhanced ion-scale wave activity associated with deviations from local thermodynamic equilibrium in ion velocity distribution functions (VDFs) observed by the PSP/Solar Probe Analyzers-Ion (SPAN-I). Dense beams (secondary populations) were present in the proton VDFs during this wave activity. Using bi-Maxwellian fits to the proton VDFs, we found that the density of the proton beam population increased during the wave activity and, unexpectedly, surpassed the core population at certain intervals. Interestingly, the wave power was reduced during the intervals when the beam population density exceeded the core density. The drift velocity of the beams decreases from 0.9 to 0.7 of the Alfvén speed and the proton core shows a higher temperature anisotropy ($T_\perp/T_\parallel>2.5$) during these intervals. We conclude that the observations during these intervals are consistent with a reconnection event during a heliospheric current sheet crossing. During this event, $α$ particle parameters (density, velocity, and temperature anisotropy) remained nearly constant. Using linear analysis, we examined how the proton beam drives instability or wave dissipation. Furthermore, We investigated the nonlinear evolution of ion kinetic instabilities using hybrid kinetic simulations. This study provides direct clues about energy transfer between particles and waves in the young solar wind.

astro-ph.SR↗

arXiv2025

Modeling hot, anisotropic ion beams in the solar wind motivated by the Parker Solar Probe observations near perihelia

Recent observations of the solar wind ions by the SPAN-I instruments on board the Parker Solar Probe (PSP) spacecraft at solar perihelia (Encounters) 4 and closer find ample evidence of complex anisotropic non-Maxwellian velocity distributions that consist of core, beam, and `hammerhead' (i.e., anisotropic beam) populations. The proton core populations are anisotropic, with T_perp/T||>1, and the beams have super-Alfvenic speed relative to the core (we provide an example from Encounter 17). The alpha-particle population show similar features as the protons. These unstable VDFs are associated with enhanced, right-hand (RH) and left-hand (LH) polarized ion-scale kinetic wave activity, detected by the FIELDS instrument. Motivated by PSP observations, we employ nonlinear hybrid models to investigate the evolution of the anisotropic hot-beam VDFs and model the growth and the nonlinear stage of ion kinetic instabilities in several linearly unstable cases. The models are initialized with ion VDFs motivated by the observational parameters. We find rapidly growing (in terms of proton gyroperiods) combined ion-cyclotron (IC) and magnetosonic (MS) instabilities, which produce LH and RH ion-scale wave spectra, respectively. The modeled ion VDFs in the nonlinear stage of the evolution are qualitatively in agreement with PSP observations of the anisotropic core and `hammerhead' velocity distributions, quantifying the effect of the ion kinetic instabilities on wind plasma heating close to the Sun. We conclude that the wave-particle interactions play an important role in the energy transfer between the magnetic energy (waves) and random particle motion leading to anisotropic solar wind plasma heating.

astro-ph.SR↗

arXiv2021

Compression of Solar Spectroscopic Observations: a Case Study of Mg II k Spectral Line Profiles Observed by NASA's IRIS Satellite

In this study we extract the deep features and investigate the compression of the Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual Mg II k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4 DN average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising.

astro-ph.SR↗

arXiv2021

Radiation Data Portal: Integration of Radiation Measurements at the Aviation Altitudes and Solar-Terrestrial Environment Observations

The impact of radiation dramatically increases at high altitudes in the Earth's atmosphere and in space. Therefore, monitoring and access to radiation environment measurements are critical for estimating the radiation exposure risks of aircraft and spacecraft crews and the impact of space weather disturbances on electronics. Addressing these needs requires convenient access to multi-source radiation environment data and enhancement of visualization and search capabilities. The Radiation Data Portal represents an interactive web-based application for search and visualization of in-flight radiation measurements. The Portal enhances the exploration capabilities of various properties of the radiation environment and provides measurements of dose rates along with information on space weather-related conditions. The Radiation Data Portal back-end is a MySQL relational database that contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS) device and the soft X-ray and proton flux measurements from the Geostationary Operational Environmental Satellite (GOES). The implemented Application Programming Interface (API) and Python routines allow a user to retrieve the database records without interaction with the web interface. As a use case of the Radiation Data Portal, we examine ARMAS measurements during an enhancement of the Solar Proton (SP) fluxes, known as Solar Proton Events (SPEs), and compare them to measurements during SP-quiet periods.

physics.space-ph↗

arXiv2020

Response of SDO/HMI observables to heating of the solar atmosphere by precipitating high-energy electrons

We perform an analysis of the line-of-sight (LOS) observables of the SDO/HMI for models of the solar atmosphere heated by precipitating high-energy electrons during solar flares. The radiative hydrodynamic (RADYN) flare models are obtained from the F-CHROMA database. The Stokes profiles for the Fe 6173 A line observed by SDO/HMI are calculated using the radiative transfer code RH1.5D assuming statistical equilibrium for atomic level populations and imposing uniform background vertical magnetic field of various strength. The SDO/HMI observing sequence and LOS data processing pipeline algorithm are applied to derive the observables (continuum intensity, line depth, Doppler velocity, LOS magnetic field). Our results reveal that the strongest deviations of the observables from the actual spectroscopic line parameters are found for the model with a total energy deposited of $E_{total}=1.0\times{}10^{12}$ erg cm$^{-2}$, injected with power-law spectral index of $δ=3$ above a low-energy cutoff of $E_{c}=25$ keV. The magnitudes of the velocity and magnetic field deviations depend on the imposed magnetic field, and can reach 0.35 km/s for LOS velocities, 90 G for LOS magnetic field, and 3\% for continuum enhancement for the 1000 G imposed LOS magnetic field setup. For $E_{total}\geq{}3.0\times{}10^{11}$ erg cm$^{-2}$ models, the velocity and magnetic field deviations are most strongly correlated with the energy flux carried by $\sim$50 keV electrons, and the continuum enhancement is correlated with the synthesized $\sim$55-60 keV hard X-ray photon flux. The relatively low magnitudes of perturbations of the observables and absence of magnetic field sign reversals suggest that the considered radiative hydrodynamic beam heating models augmented with the uniform vertical magnetic field setups cannot explain strong transient changes found in the SDO/HMI observations.

astro-ph.SR↗

arXiv2019

Statistical Properties of Soft X-ray emission of solar flares

We present a statistical analysis of properties of Soft X-Ray (SXR) emission, plasma temperature (T), and emission measure (EM), derived from GOES observations of flares in 2002-2017. The temperature and emission measures are obtained using the TEBBS algorithm (Ryan et al. 2012), which delivers reliable results together with uncertainties even for weak B-class flare events. More than 96% of flares demonstrate a sequential appearance of T, SXR, and EM maxima, in agreement with the expected behavior of the chromospheric evaporation process. The relative number of such flares increases with increasing the SXR flux maximum. The SXR maximum is closer in time to the T maximum for B-class flares than for >=C-class flares, while it is very close to the EM maximum for M- and X-class flares. We define flares as "T-controlled" if the time interval between the SXR and T maxima is at least two times shorter than the interval between the EM and SXR maxima, and as "EM-controlled" if the time interval between the EM and SXR maxima is at least two times shorter than the interval between the SXR and T maxima. For any considered flare class range, the T-controlled events compared to EM-controlled events have: a) higher EM but lower T; b) longer durations and shorter relative growth times; c) longer FWHM and characteristic decay times. Interpretation of these statistical results based on analysis of a single loop dynamics suggests that for flares of the same class range, the T-controlled events can be developed in longer loops than the EM-controlled events.

astro-ph.SR↗

arXiv2018

Statistical Study of Chromospheric Evaporation in Impulsive Phase of Solar Flares

We present a statistical study of chromospheric evaporation in solar flares using simultaneous observations by the RHESSI X-ray telescope and the IRIS UV spectrograph. The results are compared with radiation hydrodynamic flare models from the F-CHROMA RADYN database. For each event, we study spatially-resolved Doppler shifts of spectral lines formed in the transition region (C\,II\,1334.5\,Å) and hot coronal plasma (Fe\,XXI\,1354.1\,Å) to investigate the dynamics of the solar atmosphere during the flare impulsive phase. We estimate the energy fluxes deposited by high-energy electrons using X-ray imaging spectroscopy and assuming the standard thick-target model. Using the RADYN flare models, the RH 1.5D radiative transfer code and the Chianti atomic line database, we calculate C\,II and Fe\,XXI line profiles and compare with the observations. While the RADYN models predict a correlation between the Doppler shifts and deposited energy flux for both lines, this was only observed in the C II data. Several quantitative discrepancies are found between the observations and models: the Fe\,XXI Doppler shifts are substantially stronger in the models than in the data; the C\,II mean blueshifts are absent in the observations but predicted by the models. The transition energy between `gentle' and `explosive' evaporation regimes estimated from the observations ($2-8\times{}10^{9}$~erg~cm$^{-2}$~s$^{-1}$) and derived from the models ($2.2-10.1\times{}10^{9}$~erg~cm$^{-2}$~s$^{-1}$) are comparable with each other. The results illustrate relationships among the processes of chromospheric evaporation, response of the colder layers, and the flare energy flux deposited by high-energy electrons, although demonstrating discrepancy between analyzed observations and RADYN models.

astro-ph.SR↗

arXiv2018

Relationships Between Characteristics of the Line-of-Sight Magnetic Field and Solar Flare Forecasts

We analyze the relationship between the flare X-ray peak flux, and characteristics of the Polarity Inversion Line (PIL) and Active Regions (AR), derived from line-of-sight (LOS) magnetograms. The PIL detection algorithm based on a magnetogram segmentation procedure is applied for each AR with 1 hour cadence. The PIL and AR characteristics are associated with the AR flare history and divided into flaring and non-flaring cases. Effectiveness of the derived characteristics for flare forecasting is determined by the number of non-flaring cases separated from flaring cases by a certain threshold, and by their Fisher ranking score. The Support Vector Machine (SVM) classifier trained only on the PIL characteristics is used for the flare prediction. We have obtained the following results: (1) the PIL characteristics are more effective than global characteristics of ARs, (2) the highest True Skill Statistics (TSS) values of 0.76$\pm$0.03 for $\geq$M1.0 flares and 0.84$\pm$0.07 for $\geq$X1.0 flares are obtained using the "Sigmoid" SVM kernel, (3) the TSS scores obtained using only the LOS magnetograms are slightly lower than the scores obtained using vector magnetograms, but significantly better than current expert-based predictions, (4) for prediction of $\geq$M1.0 class flares 74.4 % of all cases, and 91.2 % for $\geq$X1.0 class, can be pre-classified as negative with no significant effect on the results, (5) the inclusion of global AR characteristics does not improve the forecast. The study confirms the unique role of the PIL region characteristics in the flare initiation process, and demonstrate possibilities of flare forecasting using only the line-of-sight magnetograms.

astro-ph.SR↗

arXiv2017

Interactive Multi-Instrument Database of Solar Flares

Solar flares are complicated physical phenomena that are observable in a broad range of the electromagnetic spectrum, from radiowaves to $γ$-rays. For a more comprehensive understanding of flares, it is necessary to perform a combined multi-wavelength analysis using observations from many satellites and ground-based observatories. For efficient data search, integration of different flare lists and representation of observational data, we have developed an Interactive Multi-Instrument Database of Solar Flares (https://solarflare.njit.edu/). The web accessible database is fully functional and allows the user to search for uniquely-identified flare events based on their physical descriptors and availability of observations by a particular set of instruments. Currently, the data from three primary flare lists (GOES, RHESSI and HEK) and a variety of other event catalogs (Hinode, Fermi GBM, Konus-Wind, OVSA flare catalogs, CACTus CME catalog, Filament eruption catalog) and observing logs (IRIS and Nobeyama coverage) are integrated, and an additional set of physical descriptors (temperature and emission measure) is provided along with an observing summary, data links, and multi-wavelength light curves for each flare event since January, 2002. We envision that this new tool will allow researchers to significantly speed up the search of events of interest for statistical and case studies.

astro-ph.SR↗

arXiv2017

Investigation of Relationship Between High-Energy X-ray Sources and Photospheric and Helioseismic Impacts of X1.8 Solar Flare of October 23, 2012

The X-class solar flare of October 23, 2012, generated continuum photospheric emission and a strong helioseismic wave ("sunquake") that points to an intensive energy release in the dense part of the solar atmosphere. We study properties of the energy release with high temporal and spatial resolutions, using photospheric data from the Helioseismic Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO), and hard X-ray observations made by the Ramaty High-Energy Solar Spectroscopic Imager (RHESSI). For this analysis we use level-1 HMI data (filtergrams), obtained by scanning the Fe I line (6731~Å) with the time cadence of $\sim 3.6$ s and spatial resolution of $\sim 0.5^{\prime\prime}$ per pixel. It is found that the photospheric disturbances caused by the flare spatially coincide with the region of hard X-ray emission, but are delayed by $\lesssim 4$ seconds. This delay is consistent with predictions of the flare hydrodynamics RADYN models. However, the models fail to explain the magnitude of variations observed by the HMI. The data indicate that the photospheric impact and helioseismic wave might be caused by the electron energy flux substantially higher than that in the current flare radiative hydrodynamic models.

astro-ph.SR↗