Stellar models and asteroseismic observations of rotating stars are becoming more and more precise. However, computing pulsation frequencies of rotating stars is not a trivial effort. While some types of pulsators can be modelled with first-order approaches (the commonly used Ledoux splittings), this is not the case for many others, requiring higher-order approaches. For massive main-sequence pulsators (i.e. β Cep pulsators), the main contributing factor is the deformation of the stellar surface away from a perfect sphere. This introduces shifts in the overal pulsation frequencies, and introduces asymmetries in rotationally split multiplets, which one needs to take into account when modelling these pulsation frequencies.
In this presentation, I will discuss how the recently released oscillation code StORM (https://stellar-oscillations.org) makes it possible to include these rotational effects. I will also touch upon some of the other aspects of StORM that might make you want to use it (it’s fast!), and discuss some ongoing applications.
All stars oscillate: studying these pulsations helps understand the structure and rotation profiles of the stars. In particular, I will focus on the poorly understood transition to the red-giant phase, a complex transformation involving dramatic changes to structure and rotation. We used deep learning to infer the structure and rotation parameters of red giants to better understand their physics. I will present results obtained from analysing the seismic observations taken by Kepler of red giants and show interesting rotation behaviour.
Nonlinear resonant gravito-inertial mode coupling and asteroseismology of Kepler slowly pulsating B stars.
Oscillations in slowly Pulsating B (SPB) stars measured by the Kepler space telescope have been modelled extensively using linear pulsation models, from which internal properties such as internal mixing levels and internal rotation rates have been inferred. To account for the widely varying rotation rates that have been inferred for SPB stars the traditional approximation for rotation (TAR) is typically employed. However, such linear models cannot explain how the observed gravito-inertial modes attain their observed amplitudes. Modeling the observed amplitudes requires nonlinear pulsation models that take into account mode coupling. Resonant interactions play an important role in this respect, as they provide the strongest mode couplings. Many resonant interactions can be detected among the frequencies extracted from Kepler SPB light curves. In this seminar I will discuss these resonant interaction detections, provide an overview of contemporary models of resonant nonlinear mode coupling among gravito-inertial modes (within the TAR) and show how such models can be utilized in asteroseismic modeling of Kepler SPB stars.
Investigating the effect of turbulent mixing in F-type stars.
In stellar evolution one of the fundamental ingredients is the chemical composition. At the stellar surface, the chemical abundances change during the life of the stars due to the presence of the chemical transport mechanisms. One of these processes is atomic diffusion. This process presents great success in simulating solar-type star evolution. However, for F-type stars, the inclusion of atomic diffusion shows unrealistic chemical variations at the stellar surface, indicating the need for competing transport processes. In this talk, we present the possibility of parameterizing the effects of the competing processes using the turbulent mixing. We successfully found that it is possible to avoid the chemical over-variations at the stellar surface. We also found that the effects of radiative acceleration in F-type stars can be reproduced using turbulent mixing. Then we explored the performance of using the parameterised turbulent mixing in the inference of the global properties from a sample of observed Kepler stars. We also compared its results with the current models that neglect atomic diffusion in F-type stars. We found that considering turbulent mixing creates a difference in the inferred age of about 2%.
Milky Way helium enrichment constrained by red clump stars.
The helium mass fraction, Y, is an important constraint in stellar models. For low-mass stars, its value is usually estimated by assuming a linear helium-to-metal enrichment ratio, DY/DZ, and so obtaining Y from the measured metal mass fraction, Z. However, the behaviour of DY/DZ is uncertain, and varies significantly between methods presented in the literature.We use the luminosity of red clump (low-mass, core helium-burning) stars as a proxy for Y, and so investigate the helium enrichment history of the Galaxy. The approach combines asteroseismic results from Kepler with spectroscopy from APOGEE and astrometry from Gaia to allow red clump stars to be used in this way for the first time.
Imaging subsurface solar flows by iterative helioseismic holography.
The main input data in helioseismology consists of extremely noisy, five-dimensional (2^2 spatial+1 temporal dimensions) cross-correlations of line-of-sight velocities at the solar surface. Due to the immense size of the input data, traditional approaches like time-distance helioseismology use only parts of the seismic information. Helioseismic holography, on the other hand, is a physically motivated averaging method consisting in backpropagating solar disturbances. This way helioseismic holography uses the whole seismic information to provide feature maps. Despite its great success in farside imaging, helioseismic holography is no quantitative regularization method at all. There are quantities like subsurface flows, which are in need of nonlinear inversions using the whole seismic information. This task can be tackled by iterative helioseismic holography, which combines converging iterative regularization methods with holographic backpropagation. We will present the theoretical framework of iterative helioseismic holography and show some preliminary results for non-linear inversions. In particular we are interested in the antisymmetric part of the solar differential rotation as traditional approaches like frequency splitting are not sensitive to this quantity. Afterwards we aim to step forwards to more complicated flows like convection and meridional flows.
Developing the tools for probing solar-like oscillators.
I focus on developing the fundamental tools to better characterise stellar properties, particularly for solar-type stars and red giants. My talk is a collection of three parts. Firstly, I introduce a new tool to be implemented in stellar modelling. I prescribe the surface correction, a known type of model uncertainty and conventionally treated with free parameters, to correlate with stellar parameters. The new prescription can effectively reduce the scatter in model-derived ages. Secondly, I discuss how to test the widely used asteroseismic scaling relations for deriving stellar masses and radii. I measure the intrinsic scatter to be a few per cent, using sharp features naturally formed by stellar populations. I also revise and test the systematic offsets of those relations, providing a solid justification for their applications. Thirdly, based on these foundational studies, I illustrate how discoveries can naturally emerge from data with increased precision. I find two new types of post-mass-transfer red giants based on their stellar properties.
A synergic strategy to characterize the habitability conditions of exoplanets hosted by Solar-type stars.
We present a new synergic strategy that merges the potential of asteroseismology with solar space weather/climate techniques in order to characterize Solar-like stars and their interaction with hosted exoplanets. The method is based on the use of seismic data obtained by the space missions Kepler/K2 and TESS Transiting Exoplanet Survey Satellite, coupled with stellar activity estimates deduced from ground-based campaigns (e.g., Mount Wilson Observatory HK Project). Our investigation allows us to determine not only highly accurate fundamental parameters of the mother star and its orbiting planet, but also to study the stellar magnetic activity and the star-planet interaction: in analogy to the Sun-Earth system, it is possible to infer the mean stellar wind acting on the exoplanet in order to define the conditions of the exoplanetary environment and the erosion of its atmosphere with an impact on the habitability of the planet.
Improving Power Spectral Estimation using Multitapering: Precise asteroseismic modelling of stars, exoplanets, and beyond.
Asteroseismic time-series data have imprints of stellar oscillation modes, whose detection and characterization through time-series analysis allows us to probe stellar interiors physics. Such analyses usually occur in the Fourier domain by computing the Lomb-Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly sampled time-series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. In addition, it is designed to detect strictly periodic signals but is suboptimal for non-sinusoidal periodic or quasi-periodic signals. Here, we develop a multitaper spectral estimation method that tackles the inconsistency and bias problems of the LS periodogram. We combine this multitaper method with the Non-Uniform Fast Fourier Transform (mtNUFFT) to more precisely estimate the frequencies of asteroseismic signals that are non-sinusoidal periodic (e.g., exoplanet transits) or quasi-periodic (e.g., pressure modes). We illustrate this using a simulated and the Kepler-91 red giant light curve. Particularly, we detect the Kepler-91b exoplanet and precisely estimate its period, 6.246 +/- 0.002 days, in the frequency domain using the multitaper F-test alone. We also integrate mtNUFFT into the PBjam package to obtain a Kepler-91 age estimate of 3.96 +/- 0.48 Gyr. This improvement in age estimation relative to the APOKASC-2 (uncorrected) estimate of 4.27 +/- 0.75 Gyr illustrates that mtNUFFT has promising implications for Galactic archaeology, in addition to stellar interiors and exoplanet studies. Our method generally applies to time-domain astronomy and is implemented in the public Python package tapify, available online at https://github.com/aaryapatil/tapify.
Discovering sets of pulsators with machine learning and finding automated ways to probe their physics.
The Kepler and TESS space missions have revolutionized the field of asteroseismology by delivering light curves for millions of stars. The challenge now lies in leveraging the information present in these stars and using it to improve our physical knowledge of the internal workings of stars. Machine learning proves to be the ideal solution in this case as its predictions improve with the amount of data available. We therefore developed a machine learning pipeline with both a supervised and unsupervised learning component that allows us to 1) classify the light curves according to their stellar variability type and 2) provide us with new insights into the physical relations that govern them. In this talk, we will in particular focus on how our methodology can be used to study the physical interplay between the rotation and pulsations of γ Doradus stars.