J/MNRAS/465/2634 Kepler and K2 best candidates for planets (Armstrong+, 2017)
Transit shapes and self-organizing maps as a tool for ranking planetary
candidates: application to Kepler and K2.
Armstrong D.J., Pollacco D., Santerne A.
<Mon. Not. R. Astron. Soc., 465, 2634-2642 (2017)>
=2017MNRAS.465.2634A 2017MNRAS.465.2634A (SIMBAD/NED BibCode)
ADC_Keywords: Stars, double and multiple ; Exoplanets
Keywords: methods: data analysis - methods: miscellaneous -
methods: statistical - planets and satellites: detection -
planets and satellites: general - binaries: eclipsing
Abstract:
A crucial step in planet hunting surveys is to select the best
candidates for follow-up observations, given limited telescope
resources. This is often performed by human 'eyeballing', a time
consuming and statistically awkward process. Here, we present a new,
fast machine learning technique to separate true planet signals from
astrophysical false positives. We use self-organizing maps (SOMs) to
study the transit shapes of Kepler and K2 known and candidate planets.
We find that SOMs are capable of distinguishing known planets from
known false positives with a success rate of 87.0 per cent, using the
transit shape alone. Furthermore, they do not require any candidate to
be dispositioned prior to use, meaning that they can be used early in
a mission's lifetime. A method for classifying candidates using a SOM
is developed, and applied to previously unclassified members of the
Kepler Objects of Interest (KOI) list as well as candidates from the
K2 mission. The method is extremely fast, taking minutes to run the
entire KOI list on a typical laptop. We make PYTHON code for
performing classifications publicly available, using either new SOMs
or those created in this work. The SOM technique represents a novel
method for ranking planetary candidate lists, and can be used both
alone or as part of a larger autovetting code.
Description:
A new method for identifying the best planetary candidates for
follow-up has been developed, tested, and applied to the Kepler and K2
data sets. The SOM replies only on the transit shape, and can achieve
accuracies of nearly 90 per cent in distinguishing known Kepler
planets from false positives. We apply the technique to the
unclassified Kepler and K2 candidates, and hope the resulting rankings
will be useful to the community.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 22 6350 Output statistics for KOIs, sorted by θ1
table3.dat 22 184 Output statistics for K2 objects,
sorted by θ1
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See also:
V/133 : Kepler Input Catalog (Kepler Mission Team, 2009)
IV/34 : K2 Ecliptic Plane Input Catalog (EPIC) (Huber+, 2017)
Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- KIC Kepler ID (KIC NNNNNNNN in Simbad)
11- 16 F6.3 --- theta1 Output statistic θ1 (1)
18- 22 F5.3 --- theta2 Output statistic θ2 (1)
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Note (1): Values of θ1 and θ2 above 0.5 represent planets,
and those less than 0.5 false positives.
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Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- K2 Kepler ID (EPIC NNNNNNNN in Simbad)
11- 16 F6.3 --- theta1 Output statistic θ1 (1)
18- 22 F5.3 --- theta2 Output statistic θ2 (1)
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Note (1): Values of θ1 and θ2 above 0.5 represent planets,
and those less than 0.5 false positives.
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 19-Nov-2018