Physics of Eclipsing Binaries. V. General Framework for Solving the Inverse Problem
| Autor: | Joseph Giammarco, Kelly Hambleton, Angela Kochoska, Herbert Pablo, Andrej Prša, Kyle E. Conroy, Michael Abdul-Masih, David Jones, Daniel R. Hey |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Physical sciences
Star (graph theory) Astronomy & Astrophysics 01 natural sciences Set (abstract data type) 03 medical and health sciences symbols.namesake 0103 physical sciences Code (cryptography) Instrumentation and Methods for Astrophysics (astro-ph.IM) 010303 astronomy & astrophysics Solar and Stellar Astrophysics (astro-ph.SR) 030304 developmental biology computer.programming_language Earth and Planetary Astrophysics (astro-ph.EP) Physics 0303 health sciences Science & Technology Process (computing) Astronomy and Astrophysics Observable Python (programming language) Inverse problem LIGHT Astrophysics - Solar and Stellar Astrophysics Space and Planetary Science Physical Sciences symbols Astrophysics::Earth and Planetary Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics Algorithm computer STARS Gibbs sampling Astrophysics - Earth and Planetary Astrophysics |
| Zdroj: | Conroy, K E, Kochoska, A, Hey, D, Pablo, H, Hambleton, K M, Jones, D, Giammarco, J, Abdul-Masih, M & Prša, A 2020, ' Physics of Eclipsing Binaries. V. General Framework for Solving the Inverse Problem ', Astrophysical Journal, Supplement Series, vol. 250, no. 2, 34 . https://doi.org/10.3847/1538-4365/abb4e2 |
| Popis: | PHOEBE 2 is a Python package for modeling the observables of eclipsing star systems, but until now has focused entirely on the forward-model -- that is, generating a synthetic model given fixed values of a large number of parameters describing the system and the observations. The inverse problem, obtaining orbital and stellar parameters given observational data, is more complicated and computationally expensive as it requires generating a large set of forward-models to determine which set of parameters and uncertainties best represent the available observational data. The process of determining the best solution and also of obtaining reliable and robust uncertainties on those parameters often requires the use of multiple algorithms, including both optimizers and samplers. Furthermore, the forward-model of PHOEBE has been designed to be as physically robust as possible, but is computationally expensive compared to other codes. It is useful, therefore, to use whichever code is most efficient given the reasonable assumptions for a specific system, but learning the intricacies of multiple codes presents a barrier to doing this in practice. Here we present the 2.3 release of PHOEBE (publicly available from http://phoebe-project.org) which introduces a general framework for defining and handling distributions on parameters, and utilizing multiple different estimation, optimization, and sampling algorithms. The presented framework supports multiple forward-models, including the robust model built into PHOEBE itself. accepted for publication in ApJS |
| Databáze: | OpenAIRE |
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