arXiv subjects

Shravan M. Hanasoge

Publications and source records attributed to Shravan M. Hanasoge.

At least 19 recordsLinked to original sources

arXiv2026

Multiple regular frequency spacings in $δ$ Scuti stars from automated analysis of TESS oscillation spectra

The oscillation spectra of rapidly rotating $δ$ Scuti stars are difficult to interpret because rotation modifies both the frequencies and spatial structure of the modes. Although theoretical studies predict that acoustic modes should organize into approximately equally spaced frequency sequences, systematic searches for such patterns have been limited by the absence of scalable, statistically robust detection methods. We present an automated statistical framework for identifying approximately equally spaced mode sequences in stellar oscillation spectra while quantifying the significance of each candidate detection. We apply this method to oscillation spectra of 4,681 $δ$ Scuti stars observed by the \textit{Transiting Exoplanet Survey Satellite} (TESS). The analysis identifies regular mode sequences in a large number of stars and, in many cases, reveals multiple independent regular frequency spacings within the same oscillation spectrum. While the existence of regular sequences is broadly consistent with theoretical expectations for rapidly rotating acoustic modes, the occurrence of multiple dissimilar spacings is not readily explained by current models. Differences in acoustic travel times between known island-mode families appear insufficient to account for the observed diversity of spacings, suggesting that additional mode families or other physical effects contribute to the organization of oscillation spectra of $δ$ Scuti stars. Our results provide a scalable framework for studying regular mode sequences in large stellar surveys and new observational constraints on oscillations in rapidly rotating stars.

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arXiv2026

Potential of Gaia XP Spectra in Red Giant Star Asteroseismology: A Deep-Learning Approach

Red giants are tracers of stellar evolution & Galactic structure & their asteroseismic properties, particularly large frequency separation, frequency of maximum oscillation power & dipole-mode period spacing, provide direct insight into their internal structure, masses & evolutionary states. Until now, seismic inferences on large stellar samples relied primarily on high-quality light curves from missions such as Kepler & TESS, or on moderate-resolution spectroscopy (LAMOST: R ~ 1800 & APOGEE: R ~ 22500) that clearly preserve information correlated with these seismic quantities. With Gaia XP spectra (R ~ 15-85), the possibility arises to extend asteroseismic measurements to orders of magnitude more stars, despite the much lower spectral res. . Our goal is to assess whether XP spectra retain enough information to enable reliable seismic inference for RGs. We develop hybrid CNN-LSTM models trained on RGs with seismic parameters measured from Kepler photometry. The networks learn the subtle spectral signatures, imprinted through global stellar properties, that correlate with Δν, ν_max & ΔΠ_1. The models recover all three global asteroseismic parameters from Gaia XP spectra with accuracies comparable to results based on moderate-res. surveys such as LAMOST, demonstrating that even low-res. spectrophotometry carries sufficient information for seismic prediction. Saliency analysis reveals wavelength regions most strongly associated with seismic sensitivity & highlights physically distinct spectral behaviour between RGB & RC stars. Applying our models to Gaia DR3 yields seismic predictions for more than 2.5 M bright RGs, enabling population-level asteroseismic studies on an unprecedented scale. We also identify a small subset of low-Δνred clump candidates showing unusual spectral-seismic correlations, offering new avenues for investigating evolved stellar populations.

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arXiv2025

Multi-modal encoder-decoder neural network for forecasting solar wind speed at L1

The solar wind, accelerated within the solar corona, sculpts the heliosphere and continuously interacts with planetary atmospheres. On Earth, high-speed solar-wind streams may lead to severe disruption of satellite operations and power grids. Accurate and reliable forecasting of the ambient solar-wind speed is therefore highly desirable. This work presents an encoder-decoder neural-network framework for simultaneously forecasting the daily averaged solar-wind speed for the subsequent four days. The encoder-decoder framework is trained with the two different modes of solar observations. The history of solar-wind observations from prior solar-rotations and EUV coronal observations up to four days prior to the current time form the input to two different encoders. The decoder is designed to output the daily averaged solar-wind speed from four days prior to the current time to four days into the future. Our model outputs the solar-wind speed with Root-Mean-Square Errors (RMSEs) of 55 km/s, 58 km/s, 58 km/s, and 58 km/s and Pearson correlations of 0.78, 0.66, 0.64 and 0.63 for one to four days in advance respectively. While the model is trained and validated on observations between 2010 - 2018, we demonstrate its robustness via application on unseen test data between 2019 - 2023, yielding RMSEs of 53 km/s and Pearson correlations 0.55 for a four-day advance prediction. Our encoder-decoder model thus produces much improved RMSE values compared to the previous works and paves the way for developing comprehensive multimodal deep learning models for operational solar wind forecasting.

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arXiv2024

Modelling the Center-to-Limb systematic in normal-mode-coupling measurements

Solar meridional circulation, which manifests as poleward flow near the surface, is a relatively weak flow. While meridional circulation has been measured through various local helioseismic techniques, there is a lack of consensus about the nature of the depth profile and location of return flow, owing to its small amplitude and poor signal-to-noise ratio in observations. The measurements are strongly hampered by systematic effects, whose amplitudes are comparable to the signal induced by the flow and modelling them is therefore crucial. The removal of the center-to-limb systematic, which is the largest known feature hampering the inference of meridional flow, has been heuristically performed in helioseismic analyses, but it's effect on global modes is not fully understood or modelled. Here, we propose both a way to model the center-to-limb systematic and a method for estimation of meridional flow using global helioseismic cross-spectral analysis. We demonstrate that the systematic cannot be ignored while modelling the mode-coupling cross-spectral measurement and thus is critical for the inference of meridional circulation. We also show that inclusion of a model for the center-to-limb systematic improves shallow meridional circulation estimates from cross-spectral analysis.

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arXiv2024

Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars

Magnetic fields in the stellar interiors are key candidates to explain observed core rotation rates inside solar-like stars along their evolution. Recently, asteroseismic estimates of radial magnetic field amplitudes near the hydrogen-burning shell (H-shell) inside about 24 red-giants (RGs) have been obtained by measuring frequency splittings from their power spectra. Using general Lorentz-stress (magnetic) kernels, we investigated the potential for detectability of near-surface magnetism in a 1.3 $M_{\odot}$ star of super-solar metallicity as it evolves from a mid sub-giant to a late sub-giant into an RG. Based on these sensitivity kernels, we decompose an RG into three zones - deep core, H-shell, and near-surface. The sub-giants instead required decomposition into an inner core, an outer core, and a near-surface layer. Additionally, we find that for a low-frequency g-dominated dipolar mode in the presence of a typical stable magnetic field, ~25% of the frequency shift comes from the H-shell and the remaining from deeper layers. The ratio of the subsurface tangential field to the radial field in H-burning shell decides if subsurface fields may be potentially detectable. For p-dominated dipole modes close to $ν_\rm{max}$, this ratio is around two orders of magnitude smaller in subgiant phases than the corresponding RG. Further, with the availability of magnetic kernels, we propose lower limits of field strengths in crucial layers in our stellar model during its evolutionary phases. The theoretical prescription outlined here provides the first formal way to devise inverse problems for stellar magnetism and can be seamlessly employed for slow rotators.

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arXiv2024

Probing Depth Variations of Solar Inertial Modes through Normal Mode Coupling

Recently discovered inertial waves, observed on the solar surface, likely extend to the deeper layers of the Sun. Utilizing helioseismic techniques, we explore these motions, allowing us to discern inertial-mode eigenfunctions in both radial and latitudinal orientations. We analyze $8$ years of space-based observations ($2010 - 2017$) taken by the Helioseismic and Magnetic Imager (HMI) onboard the Solar dynamic observatory (SDO) using normal-mode coupling. Coupling between same and different-degree acoustic modes and different frequency bins are measured in order to capture the various length scales of inertial modes. We detect inertial modes at high latitude with azimuthal order $t=1$ and frequency $\sim -80$ nHz. This mode is present in the entire convection zone. The presence of Rossby modes may be seen down to a depth of $\sim 0.83R_\odot$ and the Rossby signal is indistinguishable from noise below that depth for high azimuthal order. We find that the amplitudes of these modes increase with depth down to around $0.92 R_\odot$ and decrease below that depth. We find that the latitudinal eigenfunctions of Rossby modes deviate from sectoral spherical harmonics if we use a similar approach as adopted in earlier studies. We found that spatial leakage and even pure noise in the measurements of non-sectoral components can also explain the above-mentioned characteristics of the latitudinal eigenfunctions. This realization underscores the necessity for careful interpretation when considering the latitudinal eigenfunctions of Rossby modes. Exploring the depth-dependent characteristics of these modes will enable us to capture interior dynamics distinctly, separate from p-mode seismology.

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arXiv2023

A linear model for inertial modes in a differentially rotating Sun

Inertial wave modes in the Sun are of interest owing to their potential to reveal new insight into the solar interior. These predominantly retrograde-propagating modes in the solar subsurface appear to deviate from the thin-shell Rossby-Haurwitz model at high azimuthal orders. We present new measurements of sectoral equatorial inertial modes at $m>15$ where the modes appear to become progressively less retrograde compared to the canonical Rossby-Haurwitz dispersion relation in a co-rotating frame. We use a spectral eigenvalue solver to compute the spectrum of solar inertial modes in the presence of differential rotation. Focussing specifically on equatorial Rossby modes, we find that the numerically obtained mode frequencies lie along distinct ridges, one of which lies strikingly close to the observed mode frequencies in the Sun. We also find that the $n=0$ ridge is deflected strongly in the retrograde direction. This suggests that the solar measurements may not correspond to the fundamental $n=0$ Rossby-Haurwitz solutions as was initially suspected, but to a those for a higher $n$. The numerically obtained eigenfunctions also appear to sit deep within the convection zone -- unlike those for the $n=0$ modes -- which differs substantially from solar measurements and complicates inference.

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arXiv2023

Minuscule corrections to near-surface solar internal rotation using mode-coupling

The observed solar oscillation spectrum is influenced by internal perturbations such as flows and structural asphericities. These features induce splitting of characteristic frequencies and distort the resonant-mode eigenfunctions. Global axisymmertric flow -- differential rotation -- is a very prominent perturbation. Tightly constrained rotation profiles as a function of latitude and radius are products of established helioseismic pipelines that use observed Dopplergrams to generate frequency-splitting measurements at high precision. However, the inference of rotation using frequency-splittings do not consider the effect of mode-coupling. This approximation worsens for high-angular-degree modes, as they become increasingly proximal in frequency. Since modes with high angular degrees probe the near-surface layers of the Sun, inversions considering coupled modes could potentially lead to more accurate estimates of rotation very close to the surface. In order to investigate if this is indeed the case, we perform inversions for solar differential rotation, considering coupling of modes for angular degrees $160 \leq \ell \leq 300$ in the surface gravity $f$-branch and first-overtone $p$ modes. In keeping with the character of mode coupling, we carry out a non-linear inversion using an eigenvalue solver. Differences in inverted profiles for frequency splitting measurements from MDI and HMI are compared and discussed. We find that corrections to the near-surface differential rotation profile, when accounting for mode-coupling effects, are smaller than 0.003 nHz and hence are insignificant. These minuscule corrections are found to be correlated with the solar cycle. We also present corrections to even-order splitting coefficients, which could consequently impact inversions for structure and magnetic fields.

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arXiv2022

A spectral solver for solar inertial waves

Inertial waves, which are dominantly driven by the Coriolis force, likely play an important role in solar dynamics, and additionally, provide a window into the solar subsurface. The latter allows us to infer properties that are inaccessible to the traditional technique of acoustic-wave helioseismology. Thus, a full characterization of these normal modes holds promise in enabling the investigation of solar subsurface dynamics. In this work, we develop a spectral eigenvalue solver to model the spectrum of inertial waves in the Sun. We model the solar convection zone as an anelastic medium, and solve for the normal modes of the momentum and energy equations. We demonstrate that the solver can reproduce the observed mode frequencies and line-widths well, not only of sectoral Rossby modes, but also the recently observed high-frequency inertial modes. In addition, we believe that the spectral solver is a useful contribution to the numerical methods on modeling inertial modes on the Sun.

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arXiv2022

Deep learning reconstruction of sunspot vector magnetic fields for forecasting solar storms

Solar magnetic activity produces extreme solar flares and coronal mass ejections, which pose grave threats to electronic infrastructure and can significantly disrupt economic activity. It is therefore important to appreciate the triggers of explosive solar activity and develop reliable space-weather forecasting. Photospheric vector-magnetic-field data capture sunspot magnetic-field complexity and can therefore improve the quality of space-weather prediction. However, state-of-the-art vector-field observations are consistently only available from Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) since 2010, with most other current and past missions and observational facilities such as Global Oscillations Network Group (GONG) only recording line-of-sight (LOS) fields. Here, using an inception-based convolutional neural network, we reconstruct HMI sunspot vector-field features from LOS magnetograms of HMI as well as GONG with high fidelity (~ 90% correlation) and sustained flare-forecasting accuracy. We rebuild vector-field features during the 2003 Halloween storms, for which only LOS-field observations are available, and the CNN-estimated electric-current-helicity accurately captures the observed rotation of the associated sunspot prior to the extreme flares, showing a striking increase. Our study thus paves the way for reconstructing three solar cycles worth of vector-field data from past LOS measurements, which are of great utility in improving space-weather forecasting models and gaining new insights about solar activity.

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arXiv2021

Inferring solar differential rotation through normal-mode coupling using Bayesian statistics

Normal-mode helioseismic data analysis uses observed solar oscillation spectra to infer perturbations in the solar interior due to global and local-scale flows and structural asphericity. Differential rotation, the dominant global-scale axisymmetric perturbation, has been tightly constrained primarily using measurements of frequency splittings via "$a$-coefficients". However, the frequency-splitting formalism invokes the approximation that multiplets are isolated. This assumption is inaccurate for modes at high angular degrees. Analysing eigenfunction corrections, which respect cross coupling of modes across multiplets, is a more accurate approach. However, applying standard inversion techniques using these cross-spectral measurements yields $a$-coefficients with a significantly wider spread than the well-constrained results from frequency splittings. In this study, we apply Bayesian statistics to infer $a$-coefficients due to differential rotation from cross spectra for both $f$-modes and $p$-modes. We demonstrate that this technique works reasonably well for modes with angular degrees $\ell=50-291$. The inferred $a_3-$coefficients are found to be within $1$ nHz of the frequency splitting values for $\ell > 200$. We also show that the technique fails at $\ell < 50$ owing to the insensitivity of the measurement to the perturbation. These results serve to further establish mode coupling as an important helioseismic technique with which to infer internal structure and dynamics, both axisymmetric (e.g., meridional circulation) and non-axisymmetric perturbations.

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arXiv2020

A general formulation for computing spherical helioseismic sensitivity kernels while incorporating systematical effects

As helioseismology matures and turns into a precision science, modeling finite-frequency, geometric and systematical effects is becoming increasingly important. Here we introduce a general formulation for treating perturbations of arbitrary tensor rank in spherical geometry using fundamental ideas of quantum mechanics and their extensions in geophysics. We include line-of-sight projections and center-to-limb differences in line-formation heights in our analysis. We demonstrate the technique by computing a travel-time sensitivity kernel for sound-speed perturbations. The analysis produces the spherical harmonic coefficients of the sensitivity kernels, which leads to better-posed and computationally efficient inverse problems.

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arXiv2020

Application and interpretation of deep learning for identifying pre-emergence magnetic-field patterns

Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events, such as flares and coronal mass ejections and therefore understanding emergence is useful for space-weather forecasting. Evidence of any pre-emergence signatures will also shed light on sub-surface processes responsible for emergence. In this paper, we present a first analysis of emerging ARs from the Solar Dynamics Observatory/Helioseismic Emerging Active Regions (SDO/HEAR) dataset (Schunker et al. 2016) using deep convolutional neural networks (CNN) to characterize pre-emergence surface magnetic-field properties. The trained CNN classifies between pre-emergence (PE) line-of-sight magnetograms and a control set of non-emergence (NE) magnetograms with a True Skill Statistic (TSS) score of ~85%, 3h prior to emergence and ~40\%, 24h prior to emergence. Our results are better than a baseline classification TSS obtained using discriminant analysis of only the unsigned magnetic flux. We develop a network pruning algorithm to interpret the trained CNN and show that the CNN incorporates filters that respond positively as well as negatively to the unsigned magnetic flux of the magnetograms. Using synthetic magnetograms, we demonstrate that the CNN output is sensitive to the length-scale of the magnetic regions with small-scale and intense fields producing maximum CNN output and possibly a characteristic pre-emergence pattern. Given increasing popularity of deep learning, techniques developed here for interpretation of the trained CNN -- using network pruning and synthetic data -- are relevant for future applications in solar and astrophysical data analysis.

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arXiv2020

Supervised convolutional neural networks for classification of flaring and nonflaring active regions using line-of-sight magnetograms

Solar flares are explosions in the solar atmosphere that release intense bursts of short-wavelength radiation and are capable of producing severe space-weather consequences. Flares release free energy built up in coronal fields, which are rooted in active regions (ARs) on the photosphere, via magnetic reconnection. The exact processes that lead to reconnection are not fully known and therefore reliable forecasting of flares is challenging. Recently, photospheric magnetic-field data has been extensively analysed using machine learning (ML) and these studies suggest that flare-forecasting accuracy does not strongly depend on how long in advance flares are predicted (Bobra & Couvidat 2015; Raboonik et al. 2017; Huang et al. 2018). Here, we use ML to understand the evolution of AR magnetic fields before and after flares. We explicitly train convolutional neural networks (CNNs) to classify SDO/HMI line-of-sight magnetograms into ARs producing at least one M- or X-class flare or as nonflaring. We find that flaring ARs remain in flare-productive states -- marked by recall >60% with a peak of ~ 80% -- days before and after flares. We use occlusion maps and statistical analysis to show that the CNN pays attention to regions between the opposite polarities from ARs and the CNN output is dominantly decided by the total unsigned line-of-sight flux of ARs. Using synthetic bipole magnetograms, we find spurious dependencies of the CNN output on magnetogram dimensions for a given bipole size. Our results suggest that it is important to use CNN designs that eliminate such artifacts in CNN applications for processing magnetograms and, in general, solar image data.

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arXiv2020

Sensitivity kernels for inferring Lorentz stresses from normal-mode frequency splittings in the Sun

Departures from standard spherically symmetric solar models, in the form of perturbations such as global and local-scale flows and structural asphericities, result in the splitting of eigenfrequencies in the observed spectrum of solar oscillations. Drawing from prevalent ideas in normal-mode coupling theory in geophysical literature, we devise a procedure that enables the computation of sensitivity kernels for general Lorentz stress fields in the Sun. Mode coupling due to any perturbation requires careful consideration of self- and cross-coupling of multiplets. Invoking the isolated-multiplet approximation allows for limiting the treatment to purely self-coupling, requiring significantly less computational resources. We identify the presence of such isolated multiplets under the effect of Lorentz stresses in the Sun. Currently, solar missions allow precise measurements of self-coupling of multiplets via "$a$-coefficients" and the cross-spectral correlation signal which enables the estimation of the "structure coefficients". We demonstrate the forward problem for both self-coupling ($a$-coefficients) and cross-coupling (structure coefficients). In doing so, we plot the self-coupling kernels and estimate $a$-coefficients arising from a combination of deep-toroidal and surface-dipolar axisymmetric fields. We also compute the structure coefficients for an arbitrary general magnetic field (real and solenoidal) and plot the corresponding "splitting function", a convenient way to visualize the splitting of multiplets under 3D internal perturbations. The results discussed in this paper pave the way to formally pose an inverse problem, and infer solar internal magnetic fields.

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arXiv2019

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares

Solar flares - bursts of high-energy radiation responsible for severe space-weather effects - are a consequence of the occasional destabilization of magnetic fields rooted in active regions (ARs). The complexity of AR evolution is a barrier to a comprehensive understanding of flaring processes and accurate prediction. Though machine learning (ML) has been used to improve flare predictions, the potential for revealing precursors and associated physics has been underexploited. Here, we train ML algorithms to classify between vector-magnetic-field observations from flaring ARs, producing at least one M-/X-class flare, and non-flaring ARs. Analysis of magnetic-field observations accurately classified by the machine presents statistical evidence for (1) ARs persisting in flare-productive states --- characterized by AR area --- for days, before and after M- and X-class flare events, (2) systematic pre-flare build-up of free energy in the form of electric currents, suggesting that associated subsurface magnetic field is twisted, (3) intensification of Maxwell stresses in the corona above newly emerging ARs, days before first flares. These results provide new insights into flare physics and improving flare forecasting.

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arXiv2017

Iterative inversion of synthetic travel times successful at recovering sub-surface profiles of supergranular flows

We develop a helioseismic inversion algorithm that can be used to recover sub-surface vertical profiles of 2-dimensional supergranular flows from surface measurements of synthetic wave travel times. We carry out seismic wave-propagation simulations through a 2-dimensional section of a flow profile that resembles an averaged supergranule, and a starting model that has flows only at the surface. We assume that the wave measurements are entirely without realization noise for the purpose of our test. We expand the vertical profile of the supergranule stream function on a basis of B-splines. We iteratively update the B-spline coefficients of the supergranule model to reduce the travel-times differences observed between the two simulations. We carry out the exercise for four different vertical profiles peaking at different depths below the solar surface. We are able to accurately recover depth profiles of four supergranule models at depths up to $8-10\,\text{Mm}$ below the solar surface using $f-p_4$ modes, under the assumption that there is no realization noise. We are able to obtain the peak depth and the depth of the return flow for each model. A basis-resolved inversion performs significantly better than one where the flow field is inverted for at each point in the radial grid. This is an encouraging result and might act as a guide in developing more realistic inversion strategies that can be applied to supergranular flows in the Sun.

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arXiv2017

Seismic sensitivity of Normal-mode Coupling to Lorentz stresses in the Sun

Understanding the governing mechanism of solar magnetism remains an outstanding challenge in astrophysics. Seismology is the most compelling technique with which to infer the internal properties of the Sun and stars. Waves in the Sun, nominally acoustic, are sensitive to the emergence and cyclical strengthening of magnetic field, evidenced by measured changes in resonant oscillation frequencies that are correlated with the solar cycle. The inference of internal Lorentz stresses from these measurements has the potential to significantly advance our appreciation of the dynamo. Indeed, seismological inverse theory for the Sun is well understood for perturbations in composition, thermal structure and flows but, is not fully developed for magnetism, owing to the complexity of the ideal magnetohydrodynamic (MHD) equation. Invoking first-Born perturbation theory to characterize departures from spherically symmetric hydrostatic models of the Sun and applying the notation of generalized spherical harmonics, we calculate sensitivity functions of seismic measurements to the general time-varying Lorentz stress tensor. We find that eigenstates of isotropic (i.e. acoustic only) background models are dominantly sensitive to isotropic deviations in the stress tensor and much more weakly so to anisotropic stresses (and therefore challenging to infer). The apple cannot fall far from the tree.

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