Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks. (arXiv:2005.09682v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Blancato_K/0/1/0/all/0/1">Kirsten Blancato</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ness_M/0/1/0/all/0/1">Melissa Ness</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Huber_D/0/1/0/all/0/1">Daniel Huber</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lu_Y/0/1/0/all/0/1">Yuxi Lu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Angus_R/0/1/0/all/0/1">Ruth Angus</a>
Stellar variability is driven by a multitude of internal physical processes
that depend on fundamental stellar properties. These properties are our bridge
to reconciling stellar observations with stellar physics, and for understanding
the distribution of stellar populations within the context of galaxy formation.
Numerous ongoing and upcoming missions are charting brightness fluctuations of
stars over time, which encode information about physical processes such as
rotation period, evolutionary state (such as effective temperature and surface
gravity), and mass (via asteroseismic parameters). Here, we explore how well we
can predict these stellar properties, across different evolutionary states,
using only photometric time series data. To do this, we implement a
convolutional neural network, and with data-driven modeling we predict stellar
properties from light curves of various baselines and cadences. Based on a
single quarter of textit{Kepler} data, we recover stellar properties,
including surface gravity for red giant stars (with an uncertainty of
$lesssim$ 0.06 dex), and rotation period for main sequence stars (with an
uncertainty of $lesssim$ 5.2 days, and unbiased from $approx$5 to 40 days).
Shortening the textit{Kepler} data to a 27-day TESS-like baseline, we recover
stellar properties with a small decrease in precision, $sim$0.07 dex for log
$g$ and $sim$5.5 days for $P_{rm rot}$, unbiased from $approx$5 to 35 days.
Our flexible data-driven approach leverages the full information content of the
data, requires minimal feature engineering, and can be generalized to other
surveys and datasets. This has the potential to provide stellar property
estimates for many millions of stars in current and future surveys.
Stellar variability is driven by a multitude of internal physical processes
that depend on fundamental stellar properties. These properties are our bridge
to reconciling stellar observations with stellar physics, and for understanding
the distribution of stellar populations within the context of galaxy formation.
Numerous ongoing and upcoming missions are charting brightness fluctuations of
stars over time, which encode information about physical processes such as
rotation period, evolutionary state (such as effective temperature and surface
gravity), and mass (via asteroseismic parameters). Here, we explore how well we
can predict these stellar properties, across different evolutionary states,
using only photometric time series data. To do this, we implement a
convolutional neural network, and with data-driven modeling we predict stellar
properties from light curves of various baselines and cadences. Based on a
single quarter of textit{Kepler} data, we recover stellar properties,
including surface gravity for red giant stars (with an uncertainty of
$lesssim$ 0.06 dex), and rotation period for main sequence stars (with an
uncertainty of $lesssim$ 5.2 days, and unbiased from $approx$5 to 40 days).
Shortening the textit{Kepler} data to a 27-day TESS-like baseline, we recover
stellar properties with a small decrease in precision, $sim$0.07 dex for log
$g$ and $sim$5.5 days for $P_{rm rot}$, unbiased from $approx$5 to 35 days.
Our flexible data-driven approach leverages the full information content of the
data, requires minimal feature engineering, and can be generalized to other
surveys and datasets. This has the potential to provide stellar property
estimates for many millions of stars in current and future surveys.
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