arXiv subjects

Siddharth Dhanpal

Publications and source records attributed to Siddharth Dhanpal.

8 recordsLinked to original sources

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.

astro-ph.SR↗

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 \Delta\nu, \nu_max & \Delta\Pi_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-\Delta\nu red clump candidates showing unusual spectral-seismic correlations, offering new avenues for investigating evolved stellar populations.

astro-ph.SR↗

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.

astro-ph.SR↗

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.

astro-ph.SR↗

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.

astro-ph.SR↗

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.

astro-ph.SR↗

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).

astro-ph.SR↗

arXiv2018

A "no-hair" test for binary black holes

One of the consequences of the black-hole "no-hair" theorem in general relativity (GR) is that gravitational radiation (quasi-normal modes) from a perturbed Kerr black hole is uniquely determined by its mass and spin. Thus, the spectrum of quasi-normal mode frequencies have to be all consistent with the same value of the mass and spin. Similarly, the gravitational radiation from a coalescing binary black hole system is uniquely determined by a small number of parameters (masses and spins of the black holes and orbital parameters). Thus, consistency between different spherical harmonic modes of the radiation is a powerful test that the observed system is a binary black hole predicted by GR. We formulate such a test, develop a Bayesian implementation, demonstrate its performance on simulated data and investigate the possibility of performing such a test using previous and upcoming gravitational wave observations.

gr-qc↗