J/ApJ/795/64 A catalog of exoplanet physical parameters (Foreman-Mackey+, 2014)
Exoplanet population inference and the abundance of earth analogs from noisy,
incomplete catalogs.
Foreman-Mackey D., Hogg D.W., Morton T.D.
<Astrophys. J., 795, 64 (2014)>
=2014ApJ...795...64F 2014ApJ...795...64F (SIMBAD/NED BibCode)
ADC_Keywords: Stars, double and multiple ; Planets ; Models
Keywords: catalogs - methods: data analysis - methods: statistical -
planetary systems - stars: statistics
Abstract:
No true extrasolar Earth analog is known. Hundreds of planets have
been found around Sun-like stars that are either Earth-sized but on
shorter periods, or else on year-long orbits but somewhat larger.
Under strong assumptions, exoplanet catalogs have been used to make an
extrapolated estimate of the rate at which Sun-like stars host Earth
analogs. These studies are complicated by the fact that every catalog
is censored by non-trivial selection effects and detection
efficiencies, and every property (period, radius, etc.) is measured
noisily. Here we present a general hierarchical probabilistic
framework for making justified inferences about the population of
exoplanets, taking into account survey completeness and, for the first
time, observational uncertainties. We are able to make fewer
assumptions about the distribution than previous studies; we only
require that the occurrence rate density be a smooth function of
period and radius (employing a Gaussian process). By applying our
method to synthetic catalogs, we demonstrate that it produces more
accurate estimates of the whole population than standard procedures
based on weighting by inverse detection efficiency. We apply the
method to an existing catalog of small planet candidates around
G dwarf stars. We confirm a previous result that the radius distribution
changes slope near Earth's radius. We find that the rate density of
Earth analogs is about 0.02 (per star per natural logarithmic bin in
period and radius) with large uncertainty. This number is much smaller
than previous estimates made with the same data but stronger
assumptions.
Description:
The first ingredient for any probabilistic inference is a likelihood
function, a description of the probability of observing a specific
data set given a set of model parameters. In this particular project,
the data set is a catalog of exoplanet measurements and the model
parameters are the values that set the shape and normalization of the
occurrence rate density.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
fig1.dat 72 513 Simulated data for Catalog A
fig2.dat 72 474 Simulated data for Catalog B
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See also:
J/ApJ/646/505 : Catalog of nearby exoplanets (Butler+, 2006)
J/AJ/151/59 : Catalog of Earth-Like Exoplanet Survey Targets
(Chandler+, 2016)
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Byte-by-byte Description of file: fig1.dat fig2.dat
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Bytes Format Units Label Explanations
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1- 22 E22.18 d Per Period
25- 47 E23.19 Rgeo Rad [] Radius in Earth radii
51- 72 E22.18 Rgeo e_Rad Uncertainty in Rad
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History:
From electronic version of the journal
(End) Prepared by [AAS], Tiphaine Pouvreau [CDS] 22-May-2017