Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes. (arXiv:1811.03639v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Hendriks_L/0/1/0/all/0/1">Luc Hendriks</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Aerts_C/0/1/0/all/0/1">Conny Aerts</a>
We develop a novel method based on machine learning principles to achieve
optimal initiation of CPU-intensive computations for forward asteroseismic
modeling in a multi-D parameter space. A deep neural network is trained on a
precomputed asteroseismology grid containing about 62 million coherent
oscillation-mode frequencies derived from stellar evolution models. These
models are representative of the core-hydrogen burning stage of
intermediate-mass and high-mass stars. The evolution models constitute a 6D
parameter space and their predicted low-degree pressure- and gravity-mode
oscillations are scanned, using a genetic algorithm. A software pipeline is
created to find the best fitting stellar parameters for a given set of observed
oscillation frequencies. The proposed method finds the optimal regions in the
6D parameters space in less than a minute, hence providing the optimal starting
point for further and more detailed forward asteroseismic modeling in a
high-dimensional context. We test and apply the method to seven pulsating stars
that were previously modeled asteroseismically by classical grid-based forward
modeling based on a $chi^2$ statistic and obtain good agreement with past
results. Our deep learning methodology opens up the application of
asteroseismic modeling in +6D parameter space for thousands of stars pulsating
in coherent modes with long lifetimes observed by the $Kepler$ space telescope
and to be discovered with the TESS and PLATO space missions, while applications
so far were done star-by-star for only a handful of cases. Our method is open
source and can be used by anyone freely.
We develop a novel method based on machine learning principles to achieve
optimal initiation of CPU-intensive computations for forward asteroseismic
modeling in a multi-D parameter space. A deep neural network is trained on a
precomputed asteroseismology grid containing about 62 million coherent
oscillation-mode frequencies derived from stellar evolution models. These
models are representative of the core-hydrogen burning stage of
intermediate-mass and high-mass stars. The evolution models constitute a 6D
parameter space and their predicted low-degree pressure- and gravity-mode
oscillations are scanned, using a genetic algorithm. A software pipeline is
created to find the best fitting stellar parameters for a given set of observed
oscillation frequencies. The proposed method finds the optimal regions in the
6D parameters space in less than a minute, hence providing the optimal starting
point for further and more detailed forward asteroseismic modeling in a
high-dimensional context. We test and apply the method to seven pulsating stars
that were previously modeled asteroseismically by classical grid-based forward
modeling based on a $chi^2$ statistic and obtain good agreement with past
results. Our deep learning methodology opens up the application of
asteroseismic modeling in +6D parameter space for thousands of stars pulsating
in coherent modes with long lifetimes observed by the $Kepler$ space telescope
and to be discovered with the TESS and PLATO space missions, while applications
so far were done star-by-star for only a handful of cases. Our method is open
source and can be used by anyone freely.
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