Journal
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
Volume 227, Issue 2, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.3847/1538-4365/227/2/29
Keywords
binaries: close; binaries: eclipsing; methods: numerical; stars: fundamental parameters; techniques: photometric; techniques: spectroscopic
Categories
Funding
- NSF AAG [1517474]
- European Community [312844, FP7-SPACE-2011-1]
- NASA NESSF Fellowship [NNX15AR87H]
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1517474] Funding Source: National Science Foundation
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The precision of photometric and spectroscopic observations has been systematically improved in the last decade, mostly thanks to space-borne photometric missions and ground-based spectrographs dedicated to finding exoplanets. The field of eclipsing binary stars strongly benefited from this development. Eclipsing binaries serve as critical tools for determining fundamental stellar properties (masses, radii, temperatures, and luminosities), yet the models are not capable of reproducing observed data well, either because of the missing physics or because of insufficient precision. This led to a predicament where radiative and dynamical effects, insofar buried in noise, started showing up routinely in the data, but were not accounted for in the models. PHOEBE (PHysics Of Eclipsing BinariEs; http://phoebe-project.org) is an open source modeling code for computing theoretical light and radial velocity curves that addresses both problems by incorporating missing physics and by increasing the computational fidelity. In particular, we discuss triangulation as a superior surface discretization algorithm, meshing of rotating single stars, light travel time effects, advanced phase computation, volume conservation in eccentric orbits, and improved computation of local intensity across the stellar surfaces that includes the. photon-weighted mode, the. enhanced limb darkening treatment, the. better reflection treatment, and Doppler boosting. Here we present the concepts on which PHOEBE is built. and proofs of concept that demonstrate the increased model fidelity.
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