J/AJ/158/243 A search for multiplanet systems with TESS (Pearson, 2019)
A search for multiplanet systems with TESS using a Bayesian N-body retrieval
and machine learning.
Pearson K.A.
<Astron. J., 158, 243 (2019)>
=2019AJ....158..243P 2019AJ....158..243P (SIMBAD/NED BibCode)
ADC_Keywords: Stars, nearby ; Stars, bright ; Stars, diameters ;
Effective temperatures ; Abundances, [Fe/H] ; Exoplanets ; Models
Keywords: Exoplanet astronomy - Exoplanet catalogs - Exoplanet dynamics -
Observational astronomy - N-body simulations - N-body problem -
Nested sampling - Convolutional neural networks - Neural networks
Abstract:
Transiting exoplanets in multiplanet systems exhibit non-Keplerian orbits
as a result of the gravitational influence from companions, which can
cause the times and durations of transits to vary. The amplitude and
periodicity of the transit time variations are characteristic of the
perturbing planet's mass and orbit. The objects of interest from the
Transiting Exoplanet Survey Satellite (TESS) are analyzed in a uniform
way to search for transit timing variations (TTVs) with sectors 1-3 of
data. Due to the volume of targets in the TESS candidate list, artificial
intelligence is used to expedite the search for planets by vetting
nontransit signals prior to characterizing the light-curve time series.
The residuals of fitting a linear orbit ephemeris are used to search for
TTVs. The significance of a perturbing planet is assessed by comparing
the Bayesian evidence between a linear and nonlinear ephemeris, which
is based on an N-body simulation. Nested sampling is used to derive
posterior distributions for the N-body ephemeris and in order to expedite
convergence, custom priors are designed using machine learning. A
dual-input, multi-output convolutional neural network is designed to
predict the parameters of a perturbing body given the known parameters and
measured perturbation (O-C). There is evidence for three new multiplanet
candidates (WASP-18, WASP-126, TOI 193) with nontransiting companions
using the two-minute cadence observations from TESS. This approach can
be used to identify stars in need of longer radial velocity and photometric
follow-up than those already performed.
Description:
The results presented in this paper use data from Sectors 1, 2, and 3
of the TESS spacecraft. Light curves of 74 targets from the objects of
interest (TOI) catalog (Stassun et al. 2018, J/AJ/156/102) have their
time-series photometric measurements analyzed. The targets are observed
with a two-minute cadence using an 11x11 pixel subarray centered on the
target. The photometric data were processed using the Science Processing
Operations Center (SPOC) pipeline (Jenkins et al. 2016SPIE.9913E..3EJ),
which is based on the predecessor Kepler mission pipeline (Jenkins et
al. 2010; Jenkins 2017).
A custom pipeline is designed to analyze the time-series measurements
with artificial intelligence (AI) being used to vet data that does not
exhibit a transit-like shape. A vetting algorithm ensures each target
is processed in a homogeneous manner, which reduces the removal of
arbitrary choices made by human vetting.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 50 149 Stellar parameters
table3.dat 18 149 Results of artificial intelligence (AI) vetting
algorithm
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See also:
IV/38 : TESS Input Catalog - v8.0 (TIC-8) (Stassun+, 2019)
J/ApJ/809/77 : Transiting Exoplanet Survey Satellite (TESS) (Sullivan+, 2015)
J/A+A/600/A30 : Limb-darkening for TESS satellite (Claret, 2017)
J/AJ/156/102 : The TESS Input Catalog and Candidate Target List
(Stassun+, 2018)
J/ApJS/239/2 : Simulated exoplanets from TESS list of targets (Barclay+, 2018)
Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- TIC [9006668/441462736] TESS Input Catalog identifier
11- 15 F5.2 mag Tmag [5.1/15.85] Apparent TESS band magnitude
17- 21 F5.3 Rsun R* [0.154/4.176] Stellar radius
23- 26 I4 K Teff [2955/6900] Effective temperature
28- 32 F5.3 [cm/s2] log(g) [3.32/5.135] Log surface gravity
34- 38 F5.2 [Sun] [Fe/H] [-1.13/0.43] Metallicity
40- 44 F5.3 --- u1 [0.1/0.565] Linear limb darkening
46- 50 F5.3 --- u2 [0.065/0.362] Quadratic limb darkening
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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 --- TIC [9006668/441462736] TESS Input Catalog identifier
11- 18 E8.2 --- Prob [1.13e-15/1] Transit probability
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History:
From electronic version of the journal
(End) Prepared by [AAS], Tiphaine Pouvreau [CDS] 03-Feb-2020