arXiv subjects

Shravan Hanasoge

Publications and source records attributed to Shravan Hanasoge.

At least 19 recordsLinked to original sources

arXiv2026

Stellar flare detection and characterization in ZTF High-cadence light curves

Stellar flares are brief, intense increases in brightness resulting from magnetic reconnection events in stellar atmospheres. In this paper, we have developed a pipeline to detect and characterize flares in short-duration (approximately 6.2-hour) light curves from the Zwicky Transient Facility (ZTF) Extended Deep-Drilling survey. This method involves robust detrending with Tukey's biweight estimator, followed by sigma-based thresholding and visual scrutiny to identify flare candidates. It is applied to 28 million light curves, and we found 331 flaring events from 310 stars across evolutionary stages and spectral types. We estimated flare energies by assuming a blackbody emission model for 234 flares with reliable distance, and flare morphologies were classified based on flare modeling using the template by Mendoza et al.(2022). We obtained a power law correlation between energy and duration with an index of $0.19 \pm 0.01$. We also find similar power law correlation between the flare energy and $T_{decay}$, $T_{rise}$ and FWHM. This study demonstrates the power of high-cadence ground-based surveys for statistical and morphological flare studies across a broad stellar sample.

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arXiv2026

Period spacings and global seismic parameters for K2 red giants using deep learning

Gravity-mode period spacings (DPi_1) of red giants probe the stellar core directly, constraining its structure, mass and evolutionary state. Their measurement requires resolving narrow, densely spaced mixed modes and has so far relied on the four-year baseline of Kepler. Recovering DPi_1 from the much shorter (~80-day) baselines typical of K2 remains largely unexplored at the ensemble scale. We develop an automated machine-learning technique to measure global asteroseismic parameters and gravity-mode period spacings for red giants from single-campaign K2 photometry. Two deep residual neural networks take the full-resolution power spectrum of an ~80-day light curve as input, without background fitting or mode identification, and return a probability distribution for each parameter, yielding a point estimate and asymmetric uncertainties. They are trained on ~8 million synthetic spectra and evaluated on held-out synthetics, on Kepler data degraded to K2-like resolution, and on K2 observations. On Kepler data at K2-like resolution, numax and Dnu are recovered for 96% and 91% of stars with robust dispersions of 3.6% and 1.2%; DPi_1 for 22%, with a dispersion of 0.9%. Applied to 18,704 K2 red giants, the inferred numax and Dnu agree with catalogue values to 5.8% and 1.9%, the additional scatter arising from known K2 artefacts. Among the 2,059 young red giants we obtain DPi_1 for 232 stars with a median fractional uncertainty of 1.3%, following the same Dnu-DPi_1 sequence as the Kepler sample although the training set carries no imprint of that relation. We show that numax, Dnu and - for a subset of young red giants - DPi_1 can be recovered from a single ~80-day K2 campaign within an automated, probabilistic machine-learning framework. This approach can also be extended to short-baseline samples from TESS and, in future, Roman and PLATO.

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arXiv2025

Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates

Extreme precision radial velocity (EPRV) surveys usually require extensive observational baselines to confirm planetary candidates, making them resource-intensive. Traditionally, periodograms are used to identify promising candidate signals before further observational investment, but their effectiveness is often limited for low-amplitude signals due to stellar jitter. In this work, we develop a machine learning (ML) framework based on a Transformer architecture that aims to detect the presence and likely period of planetary signals in time-series spectra, even in the presence of stellar activity. The model is trained to classify whether a planetary signal exists and assign it to one of several discrete period and amplitude bins. Injection-recovery tests on randomly selected 100 epoch observation subsets from NEID solar data (2020-2022 period) show that for low-amplitude systems ($<$1 ms$^{-1}$), our model improves planetary candidate identification by a factor of two compared to the traditional Lomb-Scargle periodogram. Our ML model is built on a Vision Transformer (ViT) architecture that processes reduced representations of solar spectrum observations to predict the period and semi-amplitude of planetary signal candidates. By analyzing multi-epoch spectra, the model reliably detects planetary signals with semi-amplitudes as low as 65 cms$^{-1}$. Even under real solar noise and irregular sampling, it identifies signals down to 35 cms$^{-1}$. Comparisons with the Lomb-Scargle periodogram demonstrate a significant improvement in detecting low-amplitude planetary candidates, particularly for longer orbital periods. These results underscore the potential of machine learning to identify planetary candidates early in EPRV surveys, even from limited observational counts.

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arXiv2025

Anomalously fast core and envelope rotation in red giants

Red giants undergo dramatic and complex structural transformations as they evolve. Angular momentum is transported between the core and envelope during this epoch, a poorly understood process. Here, we infer envelope and core rotation rates from Kepler observations of $\sim$1517 red giants. While many measurements are consistent with the existing studies, our investigation reveals systematic changes in the envelope-to-core rotation ratio and we report the discovery of anomalies such as clump stars with rapidly rotating cores, and red giants with envelopes rotating faster than their cores. We propose binary interactions as a possible mechanism by which some of these cores and envelopes are spun up. These results pose challenges to current theoretical expectations and can have major implications for compact remnants born from stellar cores.

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arXiv2025

Structure and Dynamics of the Sun's Interior Revealed by Helioseismic and Magnetic Imager

High-resolution helioseismology observations with the Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO) provide a unique three-dimensional view of the solar interior structure and dynamics, revealing a tremendous complexity of the physical processes inside the Sun. We present an overview of the results of the HMI helioseismology program and discuss their implications for modern theoretical models and simulations of the solar interior.

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arXiv2025

Radial flow component of Sun's high frequency retrograde inertial waves

Solar inertial modes have the potential to surpass the diagnostic capabilities of acoustic waves in probing the deep interior of the Sun. The fulfillment of this potential requires an accurate identification and characterization of these modes. Among the set of detected inertial modes, the equatorially anti-symmetric "high-frequency retrograde'' (HFR) modes has attracted special interest because numerical studies have suggested that they are not purely toroidal, as initial observations suggested, and predicted that they would possess a significant radial flow signal at depth. Here, we analyze $\sim$13 years of HMI/SDO 5$^\circ$ ring tiles, and discover a horizontal-divergence signal, directly connected to radial flows, in the near surface layers of the Sun. We demonstrate that this signal is indeed part of the HFR modes and not spatial leakage from prograde flows associated with magnetic regions. The amplitudes of the horizontal divergence are approximately half that associated with radial vorticity. We also report the presence of a ridge of enhanced power, although with a signal-to-noise ratio of 0.3, in the retrograde frequencies that coincides with the HFR latitudinal overtones reported by models. Using numerical linear models we find reasonable agreement with observations, though future work on boundary considerations and the inclusion of the near-surface may improve future inferences. This is the first instance where numerical studies of solar inertial modes have guided observations, giving further confidence to past inferences that rely upon numerical models.

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arXiv2025

Computational Astrophysics, Data Science & AI/ML in Astronomy: A Perspective from Indian Community

In contemporary astronomy and astrophysics (A&A), the integration of high-performance computing (HPC), big data analytics, and artificial intelligence/machine learning (AI/ML) has become essential for advancing research across a wide range of scientific domains. These tools are playing an increasingly pivotal role in accelerating discoveries, simulating complex astrophysical phenomena, and analyzing vast amounts of observational data. For India to maintain and enhance its competitive edge in the global landscape of computational astrophysics and data science, it is crucial for the Indian A&A community to fully embrace these transformative technologies. Despite limited resources, the expanding Indian community has already made significant scientific contributions. However, to remain globally competitive in the coming years, it is vital to establish a robust national framework that provides researchers with reliable access to state-of-the-art computational resources. This system should involve the regular solicitation of computational proposals, which can be assessed by domain experts and HPC specialists, ensuring that high-impact research receives the necessary support. By building such a system, India can cultivate the talent, infrastructure, and collaborative environment necessary to foster world-class research in computational astrophysics and data science.

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arXiv2024

Quadratic frequency dispersion in the oscillations of intermediate-mass stars

Asteroseismology, the study of stellar vibration, has met with great success, shedding light on stellar interior structure, rotation, and magnetism. Prominently known as $δ$ Scutis, intermediate-mass main-sequence oscillators that often exhibit rapid rotation and possess complex internal stratification, are important targets of asteroseismic study. $δ$ Scuti pulsations are driven by the $κ$ (opacity) mechanism, resulting in a set of acoustic modes that can be challenging to interpret. Here, we apply machine learning to identify new patterns in the pulsation frequencies of $δ$ Scuti stars, discovering resonances spaced according to quadratic functions of integer mode indices. This unusual connection between mode frequencies and indices suggests that rotational influence may play an important role in determining the frequencies of these acoustic oscillations.

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arXiv2024

Magnetic flux in the Sun emerges unaffected by supergranular-scale surface flows

Magnetic flux emergence from the convection zone into the photosphere and beyond is a critical component of the behaviour of large-scale solar magnetism. Flux rarely emerges amid field-free areas at the surface, but when it does, the interaction between magnetism and plasma flows can be reliably explored. Prior ensemble studies identified weak flows forming near emergence locations, but the low signal-to-noise required averaging over the entire dataset, erasing information about variation across the sample. Here, we apply deep learning to achieve improved signal-to-noise, enabling a case-by-case study. We find that these associated flows are dissimilar across instances of emergence and also occur frequently in the quiet convective background. Our analysis suggests diminished influence of supergranular-scale convective flows and magnetic buoyancy on flux rise. Consistent with numerical evidence, we speculate that small-scale surface turbulence and / or deep-convective processes play an outsize role in driving flux emergence.

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arXiv2023

Asteroseismology applied to constrain structure parameters of δ Scuti stars

Asteroseismology is a powerful tool to probe the structure of stars. Space-borne instruments like CoRoT, Kepler and TESS have observed the oscillations of numerous stars, among which δ Scutis are particularly interesting owing to their fast rotation rates and complex pulsation mechanisms. In this work, we inferred model-dependent masses, metallicities and ages of 60 δ Scuti stars from their photometric, spectroscopic and asteroseismic observations using least-squares minimization. These statistics have the potential to explain why only a tiny fraction of δ Sct stars pulsate in a very clean manner. We find most of these stars with masses around 1.6 {M_\odot} and metallicities below Z = 0.010. We observed a bimodality in age for these stars, with more than half the sample younger than 30 Myr, while the remaining ones were inferred to be older, i.e., hundreds of Myrs. This work emphasizes the importance of the large-frequency separation ({Δν}) in studies of δ Scuti stars. We also designed three machine learning (ML) models that hold the potential for inferring these parameters at lower computational cost and much more rapidly. These models further revealed that constraining dipole modes can help in significantly improving age estimation and that radial modes succinctly encode information pertaining to stellar luminosity and temperature. Using the ML models, we also gained qualitative insight into the importance of stellar observables in estimating mass, metallicity, and age. The effective surface temperature T eff strongly affects the inference of all structure parameters and the asteroseismic offset parameter ε plays an essential role in the inference of age.

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arXiv2023

Inferring coupling strengths of mixed-mode oscillations in red-giant stars using deep learning

Asteroseismology is a powerful tool that may be applied to shed light on stellar interiors and stellar evolution. Mixed modes, behaving as acoustic waves in the envelope and buoyancy modes in the core, are remarkable because they allow for probing the radiative cores and evanescent zones of red-giant stars. Here, we have developed a neural network that can accurately infer the coupling strength, a parameter related to the size of the evanescent zone, of solar-like stars in $\sim$5 milliseconds. In comparison with existing methods, we found that only $\sim$43\% inferences were in agreement to within a difference of 0.03 on a sample of $\sim$1,700 \textit{Kepler} red giants. To understand the origin of these differences, we analyzed a few of these stars using independent techniques such as the Monte Carlo Markov Chain method and Echelle diagrams. Through our analysis, we discovered that these alternate techniques are supportive of the neural-net inferences. We also demonstrate that the network can be used to yield estimates of coupling strength and large period separation in stars with structural discontinuities. Our findings suggest that the rate of decline in the coupling strength in the red-giant branch is greater than previously believed. These results are in closer agreement with calculations of stellar-evolution models than prior estimates, further underscoring the remarkable success of stellar-evolution theory and computation. Additionally, we show that the uncertainty in measuring large-period separation increases rapidly with diminishing coupling strength.

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arXiv2022

Exploring the Solar Poles: The Last Great Frontier of the Sun

Despite investments in multiple space and ground-based solar observatories by the global community, the Sun's polar regions remain unchartered territory - the last great frontier for solar observations. Breaching this frontier is fundamental to understanding the solar cycle - the ultimate driver of short-to-long term solar activity that encompasses space weather and space climate. Magnetohydrodynamic dynamo models and empirically observed relationships have established that the polar field is the primary determinant of the future solar cycle amplitude. Models of solar surface evolution of tilted active regions indicate that the mid to high latitude surges of magnetic flux govern dynamics leading to the reversal and build-up of polar fields. Our theoretical understanding and numerical models of this high latitude magnetic field dynamics and plasma flows - that are a critical component of the sunspot cycle - lack precise observational constraints. This limitation compromises our ability to observe the enigmatic kilo Gauss polar flux patches and constrain the polar field distribution at high latitudes. The lack of these observations handicap our understanding of how high latitude magnetic fields power polar jets, plumes, and the fast solar wind that extend to the boundaries of the heliosphere and modulate solar open flux and cosmic ray flux within the solar system. Accurate observation of the Sun's polar regions, therefore, is the single most outstanding challenge that confronts Heliophysics. This paper argues the scientific case for novel out of ecliptic observations of the Sun's polar regions, in conjunction with existing, or future multi-vantage point heliospheric observatories. Such a mission concept can revolutionize the field of Heliophysics like no other mission concept has - with relevance that transcends spatial regimes from the solar interior to the heliosphere.

astro-ph.IM↗

arXiv2022

Measuring frequency and period separations in red-giant stars using machine learning

Asteroseismology is used to infer the interior physics of stars. The \textit{Kepler} and TESS space missions have provided a vast data set of red-giant light curves, which may be used for asteroseismic analysis. These data sets are expected to significantly grow with future missions such as \textit{PLATO}, and efficient methods are therefore required to analyze these data rapidly. Here, we describe a machine learning algorithm that identifies red giants from the raw oscillation spectra and captures \textit{p} and \textit{mixed} mode parameters from the red-giant power spectra. We report algorithmic inferences for large frequency separation ($Δν$), frequency at maximum amplitude ($ν_{max}$), and period separation ($ΔΠ$) for an ensemble of stars. In addition, we have discovered $\sim$25 new probable red giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra analyzed by the method, among which four are binary candidates which appear to possess red-giant counterparts. To validate the results of this method, we selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages ranging from subgiants to red clumps, and compare inferences of $Δν$, $ΔΠ$, and $ν_{max}$ with estimates obtained using other techniques. The power of the machine-learning algorithm lies in its speed: it is able to accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on a modern computer (single core of the Intel Xeon Platinum 8280 CPU).

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arXiv2022

Imaging the Sun's near-surface flows using mode-coupling analysis

The technique of normal-mode coupling is a powerful tool with which to seismically image non-axisymmetric phenomena in the Sun. Here we apply mode coupling in the Cartesian approximation to probe steady, near-surface flows in the Sun. Using Doppler cubes obtained from the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory, we perform inversions on mode-coupling measurements to show that the resulting divergence and radial vorticity maps at supergranular length scales ($\sim$30 Mm) near the surface compare extremely well with those obtained using the Local Correlation Tracking method. We find that the Pearson correlation coefficient is $\geq$ 0.9 for divergence flows, while $\geq$ 0.8 is obtained for the radial vorticity.

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arXiv2021

Investigating toroidal flows in the Sun using normal-mode coupling

Helioseismic observations have provided valuable datasets with which to pursue the detailed investigation of solar interior dynamics. Among various methods to analyse these data, normal-mode coupling has proven to be a powerful tool, used to study Rossby waves, differential rotation, meridional circulation, and non-axisymmetric multi-scale subsurface flows. Here, we invert mode-coupling measurements from Helioseismic Magnetic Imager (HMI) and Michelson Doppler Imager (MDI) to obtain mass-conserving toroidal convective flow as a function of depth, spatial wavenumber, and temporal frequency. To ensure that the estimates of velocity magnitudes are proper, we also evaluate correlated realization noise, caused by the limited visibility of the Sun. We benchmark the near-surface inversions against results from Local Correlation Tracking (LCT). Convective power likely assumes greater latitudinal isotropy with decrease in spatial scale of the flow. We note an absence of a peak in toroidal-flow power at supergranular scales, in line with observations that show that supergranulation is dominantly poloidal in nature.

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arXiv2021

Analyzing supergranular power spectra using helioseismic normal-mode coupling

Normal-mode coupling is a technique applied to probe the solar interior using surface observations of oscillations. The technique, which is straightforward to implement, makes more use of the seismic information in the wavefield than other comparable local imaging techniques and therefore has the potential to significantly improve current capabilities. Here, we examine supergranulation power spectra using mode-coupling analyses of intermediate-to-high-degree modes by invoking a Cartesian-geometric description of wave propagation under the assumption that the localized patches are much smaller in size than the solar radius. We extract the supergranular power spectrum and compare the results with prior helioseismic studies. Measurements of the dispersion relation and life times of supergranulation, obtained using near surface modes (f and p$_1$), are in accord with the literature. We show that the cross-coupling between the p$_2$ and p$_3$ acoustic modes, which are capable of probing greater depths, are also sensitive to supergranulation.

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arXiv2020

Validating inversions for toroidal flows using normal-mode coupling

Normal-mode coupling is a helioseismic technique that uses measurements of mode eigenfunctions to infer the interior structure of the Sun. This technique has led to insights into the evolution and structure of toroidal flows in the solar interior. Here, we validate an inversion algorithm for normal-mode coupling by generating synthetic seismic measurements associated with input flows and comparing the input and inverted velocities. We study four different cases of input toroidal flows and compute synthetics that take into account the partial visibility of the Sun. We invert the synthetics using Subtractive Optimally Localized Averages (SOLA) and also try to mitigate the systematics of mode leakage. We demonstrate that, ultimately, inversions are only as good as the model we assume for the correlation between flow velocities.

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arXiv2020

Solar wind prediction using deep learning

Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magnetosphere has space-weather consequences such as geomagnetic storms. Accurately predicting the solar wind through measurements of the spatio-temporally evolving conditions in the solar atmosphere is important but remains an unsolved problem in heliophysics and space-weather research. In this work, we use deep learning for prediction of solar wind (SW) properties. We use Extreme Ultraviolet images of the solar corona from space based observations to predict the SW speed from the NASA OMNIWEB dataset, measured at Lagragian point 1. We evaluate our model against autoregressive and naive models, and find that our model outperforms the benchmark models, obtaining a best-fit correlation of 0.55 $\pm$ 0.03 with the observed data. Upon visualization and investigation of how the model uses data to make predictions, we find higher activation at the coronal holes for fast wind prediction ($\approx$ 3 to 4 days prior to prediction), and at the active regions for slow wind prediction. These trends bear an uncanny similarity to the influence of regions potentially being the sources of fast and slow wind, as reported in literature. This suggests that our model was able to learn some of the salient associations between coronal and solar wind structure without built-in physics knowledge. Such an approach may help us discover hitherto unknown relationships in heliophysics datasets.

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