arXiv subjects

Vishal Upendran

Publications and source records attributed to Vishal Upendran.

At least 19 recordsLinked to original sources

arXiv2026

Chromospheric and Transition Region Responses of activities at the base of Coronal Plumes

We present the identification of the co-spatial and co-temporal small scale jets -- sometimes also named as jetlets -- at the base of the coronal hole plume associated with chromospheric and transition region (TR) flows. We identified 19 jets with coordinated observations recorded on March 19, 2016 using the Solar Dynamics Observatory (SDO) and the Interface Region Imaging Spectrograph (IRIS). Our analysis of \ion{Si}{4} 1393.7~{\AA} line suggests blueshifts mostly in the range of 10 to 33~km~s$^{-1}$, but few less than -10~km~s$^{-1}$, while analysis of co-spatial \ion{Mg}{2}~h~\&~k lines show both blueshifts and redshifts in the range of {--}9 to 17~km~s$^{-1}$. We observed highly asymmetric, enhanced intensity in \ion{Si}{4} line during these events. The upflows observed in transition region through \ion{Si}{4} is strongly correlated with chromospheric downflows observed in \ion{Mg}{2}. We interpret these results as a signature of interchange reconnections creating bi-directional flows -- giving rise to upflows (downflows) in transition region (chromosphere).

astro-ph.SR↗

arXiv2026

The Study of Quasi-Periodic Pulsations in Solar and Stellar Flares with SKA

An intensively studied phenomenon which is not described by the standard flare model are quasi-periodic pulsations (QPP) of the flaring emission. As analysis of the QPP phenomenon intrinsically requires a combination of high time and spatial resolutions, especially in the radio band, the unprecedented capabilities of SKA offer us a unique opportunity to reach a breakthrough progress in the observational study of QPP. The SKA-Mid-frequency band falls in a unique window where both coherent emissions from particle acceleration sites and incoherent gyrosynchrotron emissions from non-thermal particles in coronal loops can be studied. With an additional polarisation dimension and the capability to perform wideband spectroscopic imaging, the QPPs in gyrosynchrotron emission ($\ge 1$~GHz) and plasma emission will help understand the local magnetic field modulation due to active phenomena and the response seen in the particle acceleration observable below ~600~MHz. An incomplete list of specific science questions to be addressed with SKA includes (a) the role of QPP in the energy partition in flares, (b) seismology of flaring sites by QPP of different classes, (c ) differences and similarities between QPP in solar and stellar flares, (d) advancing the standard flare model, (e) the physics of repetitive magnetic reconnection: spontaneous vs induced, (f) ML techniques in the detection, classification and analysis of QPP, (g) QPP in weak flares. The latter topic could be especially advanced with SKA which will allow for high-cadence high fidelity radio imaging of weak energy release events.

astro-ph.SR↗

arXiv2026

Near-Sun Switchbacks Show Statistical Signatures of Solar Activity

The large amplitude, Alfv\'enic deflections in the solar wind, called magnetic switchbacks, are a ubiquitous feature of the inner heliosphere, yet their origin and variability remain poorly understood. We investigate the dependence of large amplitude magnetic fluctuations in the inner heliosphere on both heliocentric distance and global solar activity. Using Parker Solar Probe observations, we quantify switchback deflections through their normalised deflection angle $z$. We examine both their distribution and the probability of large events, combining radial binning, sunspot-number-based activity classification, and regression analysis. We find a statistically significant but weak dependence of switchback properties on solar activity, with a decrease in large deflections at higher activity levels, alongside a modest increase with heliocentric distance. The weak activity trend suggests that multiple processes act simultaneously. Solar-cycle variations in coronal magnetic topology may modulate switchback generation at the source, while nonlinear in situ effects in the solar wind may partially evolve these signatures. Our results indicate that switchbacks retain only a limited imprint of solar activity, reflecting a coupled interplay between coronal origin and in situ evolution. This study provides a quantitative framework to disentangle activity and radial effects, and highlights the need for multi-parameter and multi-spacecraft analyses to fully understand the origin and evolution of switchbacks.

astro-ph.SR↗

arXiv2026

Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model

We present SDO2IRIS$^2$: a visual transformer model that translates a combination of images of the chromosphere and transition region (TR), observed by AIA, and a line-of-sight magnetogram, provided by HMI, into temperature, line-of-sight velocity (v$_{los}$), velocity of the turbulent motions (v$_{turb}$), and electron density (n$_{e}$) in the chromosphere. Using the thermodynamic variables obtained from the inversion of the chromospheric lines Mg II h&k, observed by IRIS, as the target of the model, and the intensity images in the chromosphere and TR, and the photospheric magnetogram as the input, the predicted T and n$_{e}$ show a strong correlation ($\approx 0.80$) for $\approx$80% of the test inverted data, a moderate-to-strong correlation ($\approx0.63$) for 70% of the v$_{turb}$ of the target test inverted data, while for the $v_{los}$, the correlation is weak. Therefore, the predicted values by SDO2IRIS$^2$ may be used as an estimation of the thermodynamics in the chromosphere, either as a stand-alone result or as complementary information to other chromospheric data observed simultaneously. The execution time employed by SDO2IRIS$^2$ to obtain the thermodynamic values in the chromosphere is of the order of a few minutes, being $\le10$ minutes when using a CPU, and $\le5$ minutes when using a GPU. SDO2IRIS$^2$ opens a new avenue for the use of SDO data thanks to the inversions provided by IRIS observables.

astro-ph.SR↗

arXiv2025

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar EUV spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.

astro-ph.SR↗

arXiv2025

Surya: Foundation Model for Heliophysics

Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, including eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products. Surya employs a spatiotemporal transformer architecture with spectral gating and long--short range attention, pretrained on high-resolution solar image forecasting tasks and further optimized through autoregressive rollout tuning. Zero-shot evaluations demonstrate its ability to forecast solar dynamics and flare events, while downstream fine-tuning with parameter-efficient Low-Rank Adaptation (LoRA) shows strong performance on solar wind forecasting, active region segmentation, solar flare forecasting, and EUV spectra. Surya is the first foundation model in heliophysics that uses time advancement as a pretext task on full-resolution SDO data. Its novel architecture and performance suggest that the model is able to learn the underlying physics behind solar evolution.

astro-ph.SR↗

arXiv2025

X-class flare on Dec 31, 2023, observed by the Solar Ultraviolet Imaging Telescope on board Aditya-L1

We present the multi-wavelength study of the ejection of a plasma blob from the limb flare SOL2023-12-31T21:36:00 from NOAA 13536 observed by the Solar Ultraviolet Imaging Telescope (SUIT) on board Aditya-L1. We use SUIT observations along with those from Atmospheric Imaging Assembly (AIA) on board SDO and Spectrometer/Telescope for Imaging X-rays (STIX) on board Solar Orbiter to infer the kinematics and thermal nature of the ejected blob and its connection to the associated flare. The observations show that the flare was comprised of two eruptions. The blob was ejected during the first eruption and later accelerated to velocities over 1500 km/s measured at a maximum projected height of ~ 178 Mm from the Sun's surface. The acceleration of the ejected plasma blob is co-temporal with the bursty appearance of the hard X-ray light curve recorded by STIX. Radio spectrogram observations from STEREO-A/WAVES and RSTN reveal type III bursts at the same time, indicative of magnetic reconnection. DEM analysis using AIA observations suggests the plasma blob is comprised of cooler and denser plasma in comparison to the ambient corona. To the best of our knowledge, this is the first observation of such a plasma blob in the NUV, providing crucial measurements for eruption thermodynamics.

astro-ph.SR↗

arXiv2025

Comparison of plasma dynamics in Coronal Holes and Quiet Sun using flux emergence simulations

This paper presents a comparison of plasma dynamics in Coronal Holes (CHs) and Quiet Sun (QS) through 2.5D MHD flux emergence simulations. The magnetic reconnection between the emerging and the pre-existing flux leads to the formation of cool, dense plasmoids with hot boundaries, and hot & cool jets with velocities $\approx50$ km s$^{-1}$. We perform spectral synthesis in spectral lines probing transition region and coronal temperatures. CHs show reduced intensities, excess upflows (downflows), and widths during the jetting (downflow) period when compared to QS. During the jetting and downflow periods, velocity and line width of the hot spectral lines in CHs show a strong positive correlation with the vertical magnetic field at z = 0, while the intensity of the cooler lines shows a weak correlation, which is not seen in QS. During the jetting period in CH, we find upflows in Si IV to be correlated (anti-correlated) with upflows (downflows) in other lines, and downflows in CH in Si IV to be correlated (anti-correlated) with upflows (downflows) in other lines when compared to QS. During downflow, we find no strong correlation between Si IV and other line velocities. The correlation during the jetting period occurs due to coincident, co-spatial origins of the hot and cool jet, while the lack of correlation during the downflow phase suggests a decoupling of hot and cool plasma. These results demonstrate that flux emergence and reconnection with pre-existing flux in the atmosphere support a unified scenario for solar wind formation and coronal heating.

astro-ph.SR↗

arXiv2024

SPACE-SUIT: An Artificial Intelligence Based Chromospheric Feature Extractor and Classifier for SUIT

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical study, necessitating the development of automatic feature detection methods. To this end, we develop the feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and Classification using Enhanced vision techniques for SUIT, to detect and classify the solar chromospheric features to be observed from SUIT's Mg II k filter. Specifically, we target plage regions, sunspots, filaments, and off-limb structures. SPACE uses YOLO, a neural network-based model to identify regions of interest. We train and validate SPACE using mock-SUIT images developed from Interface Region Imaging Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also perform detection on Level-1 SUIT data. SPACE achieves an approximate precision of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS dataset. Given the manual labeling of our dataset, we perform "self-validation" by applying statistical measures and Tamura features on the ground truth and predicted bounding boxes. We find the distributions of entropy, contrast, dissimilarity, and energy to show differences in the features. These differences are qualitatively captured by the detected regions predicted by SPACE and validated with the observed SUIT images, even in the absence of labeled ground truth. This work not only develops a chromospheric feature extractor but also demonstrates the effectiveness of statistical metrics and Tamura features for distinguishing chromospheric features, offering independent validation for future detection schemes.

astro-ph.SR↗

arXiv2024

Surface Flux Transport Modeling using Physics Informed Neural Networks

Studying the magnetic field properties on the solar surface is crucial for understanding the solar and heliospheric activities, which in turn shape space weather in the solar system. Surface Flux Transport (SFT) modeling helps us to simulate and analyse the transport and evolution of magnetic flux on the solar surface, providing valuable insights into the mechanisms responsible for solar activity. In this work, we demonstrate the use of machine learning techniques in solving magnetic flux transport, making it accurate. We have developed a novel Physics-Informed Neural Networks (PINN)-based model to study the evolution of Bipolar Magnetic Regions (BMRs) using SFT in one-dimensional azimuthally averaged and also in two-dimensions. We demonstrate the efficiency and computational feasibility of our PINN-based model by comparing its performance and accuracy with that of a numerical model implemented using the Runge-Kutta Implicit-Explicit (RK-IMEX) scheme. The mesh-independent PINN method can be used to reproduce the observed polar magnetic field with better flux conservation. This advancement is important for accurately reproducing observed polar magnetic fields, thereby providing insights into the strength of future solar cycles. This work paves the way for more efficient and accurate simulations of solar magnetic flux transport and showcases the applicability of PINN in solving advection-diffusion equations with a particular focus on heliophysics.

astro-ph.SR↗

arXiv2024

Thermodynamic Evolution of Plumes

Plumes are considered to play an important role in the origin of solar wind. However, an understanding of their thermodynamic evolution is not complete. Here, we perform a detailed study of a plume inside a coronal hole throughout its lifetime, using the observations from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI). We find that the plume's formation is preceded by frequent occurrences of small-scale jets and jet-lets at its base, leading to the gradual development of plume haze. The plume rapidly developed within the first six hours into its well-known morphology. Light curves from all EUV channels exhibit a similar profile, suggesting its multi-thermal nature and intensity modulation over its lifespan. Moreover, the photospheric magnetic field dynamics at the plume's base are highly correlated with its light curve in 171~{\AA}. We calculate outflow velocities, observed prominently in the 171~{\AA} passband and mildly in the 193~{\AA} and 211~{\AA} passbands, with median speeds lower in higher temperature bands but occasionally comparable to the respective sound speeds. When data is averaged over larger spatial scales, the plume appears iso-thermal along its length, with constant temperature throughout its lifetime. However, an analysis of the differential emission measure at full resolution reveals the presence of higher-temperature plasma, indicating internal temperature structures within the plume. These results provide new insights into the formation, dynamics, and thermal properties of coronal plumes, placing tighter constraints on models to understand their thermodynamic evolution and potential role in the solar wind.

astro-ph.SR↗

arXiv2023

Heating and dynamics of the Solar atmosphere

The solar atmosphere shows anomalous variation in temperature, starting from the 5500 K photosphere to the million-degree Kelvin corona. The corona itself expands into the interstellar medium as the free streaming solar wind, which modulates and impacts the near-Earth space weather. The precise source regions of different structures in the solar wind, their formation height, and the heating of the solar atmosphere are inextricably linked and unsolved problems in astrophysics. Observations suggest correlations between Coronal holes (CHs), which are cool, intensity deficit structures in the solar corona, with structures in the solar wind. Observations also suggest the local plasma heating in the corona through power-law distributed impulsive events. In this thesis, we use narrowband photometric, spectroscopic, and disc-integrated emission of the solar atmosphere ranging from Near Ultraviolet to X-rays along with in-situ solar wind measurements to understand (i). the source regions of the solar wind, (ii). the underlying mechanism of solar coronal heating, and (iii). the differentiation in dynamics of CHs with the background Quiet Sun (QS) regions, which do not show any significant signature of the solar wind. We leverage machine learning and numerical modeling tools to develop solar wind forecasting codes using interpretable AI, inversion codes to infer the properties of impulsive events and to understand the differences in the thermodynamics of CHs and QS regions. We finally present a unified scenario of solar wind emergence and heating in the solar atmosphere and discuss the implications of inferences from this thesis.

astro-ph.SR↗

arXiv2022

Nanoflare Heating of the Solar Corona Observed in X-rays

The existence of the million-degree corona above the cooler photosphere is an unsolved problem in astrophysics. Detailed study of quiescent corona that exists regardless of the phase of the solar cycle may provide fruitful hints towards resolving this conundrum. However, the properties of heating mechanisms can be obtained only statistically in these regions due to their unresolved nature. Here, we develop a two-step inversion scheme based on the machine learning scheme of Upendran & Tripathi (2021a) for the empirical impulsive heating model of Pauluhn & Solanki (2007), and apply it to disk integrated flux measurements of the quiet corona as measured by the X-ray solar monitor (XSM) onboard Chandrayaan - 2. We use data in three energy passbands, viz., 1 - 1.3 keV, 1.3 - 2.3 keV, and 1 - 2.3 keV, and estimate the typical impulsive event frequencies, timescales, amplitudes, and the distribution of amplitudes. We find that the impulsive events occur at a frequency of $\approx$25 events per minute with a typical lifetime of $\approx10$ minutes. They are characterized by a power law distribution with a slope $α\leq2.0$. The typical amplitudes of these events lie in an energy range of $10^{21}$ - $10^{24}$ ergs, with a typical radiative loss of about $\approx10^3$ erg cm$^{-2}$ s$^{-1}$ in the energy range of 1 - 2.3 keV. These results provide further constraints on the properties of sub-pixel impulsive events in maintaining the quiet solar corona.

astro-ph.SR↗

arXiv2022

Global geomagnetic perturbation forecasting using Deep Learning

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 minutes into the future using only solar wind measurements as input. The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1-minute cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.

physics.space-ph↗

arXiv2021

On the formation of solar wind & switchbacks, and quiet Sun heating

The solar coronal heating in quiet Sun (QS) and coronal holes (CH), including solar wind formation, are intimately tied by magnetic field dynamics. Thus, a detailed comparative study of these regions is needed to understand the underlying physical processes. CHs are known to have subdued intensity and larger blueshifts in the corona. This work investigates the similarities and differences between CHs and QS in the chromosphere using the Mg II h & k, C II lines, and transition region using Si IV line, for regions with identical absolute magnetic flux density (|B|). We find CHs to have subdued intensity in all the ines, with the difference increasing with line formation height and |B|. The chromospheric lines show excess upflows and downflows in CH, while Si IV shows excess upflows (downflows) in CHs (QS), where the flows increase with |B|. We further demonstrate that the upflows (downflows) in Si IV are correlated with both upflows and downflows (only downflows) in the chromospheric lines. CHs (QS) show larger Si IV upflows (downflows) for similar flows in the chromosphere, suggesting a common origin to these flows. These observations may be explained due to impulsive heating via interchange (closed-loop) reconnection in CHs (QS), resulting in bidirectional flows at different heights, due to differences in magnetic field topologies. Finally, the kinked field lines from interchange reconnection may be carried away as magnetic field rotations and observed as switchbacks. Thus, our results suggest a unified picture of solar wind emergence, coronal heating, and near-Sun switchback formation.

astro-ph.SR↗

arXiv2021

Properties of the C II 1334 Å line in Coronal Hole and Quiet Sun as observed by IRIS

Coronal Holes (CHs) have subdued intensity and net blueshifts when compared to Quiet Sun (QS) at coronal temperatures. At transition region temperatures, such differences are obtained for regions with identical photospheric absolute magnetic flux density ($\vert$B$\vert$). In this work, we use spectroscopic measurements of the \car 1334~Å line from Interface Region Imaging Spectrograph (IRIS), formed at chromospheric temperatures, to investigate the intensity, Doppler shift, line width, skew, and excess kurtosis variations with $\vert$B$\vert$. We find the intensity, Doppler shift, and line widths to increase with $\vert$B$\vert$ for CHs and QS. The CHs show deficit in intensity and excess total widths over QS for regions with identical $\vert$B$\vert$. For pixels with only upflows, CHs show excess upflows over QS, while for pixels with only downflows, CHs show excess downflows over QS that cease to exist at $\vert$B$\vert$ $\le$ 40. Finally, the spectral profiles are found to be more skewed and flatter than a Gaussian, with no difference between CH and QS. These results are important in understanding the heating of the atmosphere in CH and QS, including solar wind formation, and provide further constraints on the modeling of the solar atmosphere.

astro-ph.SR↗

arXiv2021

On the Impulsive Heating of Quiet Solar Corona

The solar corona consists of a million-degree Kelvin plasma. A complete understanding of this phenomenon demands the study of Quiet Sun (QS) regions. In this work, we study QS regions in the 171 Å, 193 Å and 211 Å passbands of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), by combining the empirical impulsive heating forward model of Pauluhn & Solanki (2007) with a machine-learning inversion model that allows uncertainty quantification. We find that there are {\approx} 2--3 impulsive events per min, with a lifetime of about 10--20 min. Moreover, for all the three passbands, the distribution of power law slope α peaks above 2. Our exploration of correlations among the frequency of impulsive events and their timescales and peak energy suggests that conduction losses dominate over radiative cooling losses. All these finding suggest that impulsive heating is a viable heating mechanism in QS corona.

astro-ph.SR↗

arXiv2021

Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition

Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding models like the Magnetohydrodynamics (MHD) model or sampling spatially and temporally through sparse ground-based stations (SuperMAG). In this paper, we develop a Deep Learning model that forecasts in Spherical Harmonics space 2, replacing reliance on MHD models and providing global coverage at one minute cadence, improving over the current state-of-the-art which relies on feature engineering. We evaluate the performance in SuperMAG dataset (improved by 14.53%) and MHD simulations (improved by 24.35%). Additionally, we evaluate the extrapolation performance of the spherical harmonics reconstruction based on sparse ground-based stations (SuperMAG), showing that spherical harmonics can reliably reconstruct the global magnetic field as evaluated on MHD simulation.

physics.geo-ph↗