Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data. (arXiv:1903.10507v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Dattilo_A/0/1/0/all/0/1">Anne Dattilo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Vanderburg_A/0/1/0/all/0/1">Andrew Vanderburg</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Shallue_C/0/1/0/all/0/1">Christopher J. Shallue</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mayo_A/0/1/0/all/0/1">Andrew W. Mayo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Berlind_P/0/1/0/all/0/1">Perry Berlind</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bieryla_A/0/1/0/all/0/1">Allyson Bieryla</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Calkins_M/0/1/0/all/0/1">Michael L. Calkins</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Esquerdo_G/0/1/0/all/0/1">Gilbert A. Esquerdo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Everett_M/0/1/0/all/0/1">Mark E. Everett</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Howell_S/0/1/0/all/0/1">Steve B. Howell</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Latham_D/0/1/0/all/0/1">David W. Latham</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Scott_N/0/1/0/all/0/1">Nicholas J. Scott</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yu_L/0/1/0/all/0/1">Liang Yu</a>
For years, scientists have used data from NASA’s Kepler Space Telescope to
look for and discover thousands of transiting exoplanets. In its extended K2
mission, Kepler observed stars in various regions of sky all across the
ecliptic plane, and therefore in different galactic environments. Astronomers
want to learn how the population of exoplanets are different in these different
environments. However, this requires an automatic and unbiased way to identify
the exoplanets in these regions and rule out false positive signals that mimic
transiting planet signals. We present a method for classifying these exoplanet
signals using deep learning, a class of machine learning algorithms that have
become popular in fields ranging from medical science to linguistics. We
modified a neural network previously used to identify exoplanets in the Kepler
field to be able to identify exoplanets in different K2 campaigns, which range
in galactic environments. We train a convolutional neural network, called
AstroNet-K2, to predict whether a given possible exoplanet signal is really
caused by an exoplanet or a false positive. AstroNet-K2 is highly successful at
classifying exoplanets and false positives, with accuracy of 98% on our test
set. It is especially efficient at identifying and culling false positives, but
for now, still needs human supervision to create a complete and reliable planet
candidate sample. We use AstroNet-K2 to identify and validate two previously
unknown exoplanets. Our method is a step towards automatically identifying new
exoplanets in K2 data and learning how exoplanet populations depend on their
galactic birthplace.
For years, scientists have used data from NASA’s Kepler Space Telescope to
look for and discover thousands of transiting exoplanets. In its extended K2
mission, Kepler observed stars in various regions of sky all across the
ecliptic plane, and therefore in different galactic environments. Astronomers
want to learn how the population of exoplanets are different in these different
environments. However, this requires an automatic and unbiased way to identify
the exoplanets in these regions and rule out false positive signals that mimic
transiting planet signals. We present a method for classifying these exoplanet
signals using deep learning, a class of machine learning algorithms that have
become popular in fields ranging from medical science to linguistics. We
modified a neural network previously used to identify exoplanets in the Kepler
field to be able to identify exoplanets in different K2 campaigns, which range
in galactic environments. We train a convolutional neural network, called
AstroNet-K2, to predict whether a given possible exoplanet signal is really
caused by an exoplanet or a false positive. AstroNet-K2 is highly successful at
classifying exoplanets and false positives, with accuracy of 98% on our test
set. It is especially efficient at identifying and culling false positives, but
for now, still needs human supervision to create a complete and reliable planet
candidate sample. We use AstroNet-K2 to identify and validate two previously
unknown exoplanets. Our method is a step towards automatically identifying new
exoplanets in K2 data and learning how exoplanet populations depend on their
galactic birthplace.
http://arxiv.org/icons/sfx.gif
