2017
DOI: 10.3847/1538-3881/aa7ed8
|View full text |Cite
|
Sign up to set email alerts
|

HELIOS–RETRIEVAL: An Open-source, Nested Sampling Atmospheric Retrieval Code; Application to the HR 8799 Exoplanets and Inferred Constraints for Planet Formation

Abstract: We present an open-source retrieval code named HELIOS-RETRIEVAL, designed to obtain chemical abundances and temperature-pressure profiles from inverting the measured spectra of exoplanetary atmospheres. We use an exact solution of the radiative transfer equation, in the pure absorption limit, in our forward model, which allows us to analytically integrate over all of the outgoing rays. Two chemistry models are considered: unconstrained chemistry and equilibrium chemistry (enforced via analytical formulae). The… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

3
206
2

Year Published

2017
2017
2020
2020

Publication Types

Select...
5
4

Relationship

2
7

Authors

Journals

citations
Cited by 151 publications
(211 citation statements)
references
References 94 publications
(204 reference statements)
3
206
2
Order By: Relevance
“…We do this because in fast spectral retrieval models (e.g. Lavie et al 2017), the influence of these metals on the concentration of molecules like H 2 O and CO 2 is often neglected, i.e. (C/O) eff ≈ C/O is assumed.…”
Section: Phase Diagrams Of the Elementsmentioning
confidence: 99%
“…We do this because in fast spectral retrieval models (e.g. Lavie et al 2017), the influence of these metals on the concentration of molecules like H 2 O and CO 2 is often neglected, i.e. (C/O) eff ≈ C/O is assumed.…”
Section: Phase Diagrams Of the Elementsmentioning
confidence: 99%
“…The basics of these concepts are introduced in Trotta (2008); Skilling (2006). A full description of the approach taken here can be found in Lavie et al (2017). For similar applications and examples see Benneke & Seager (2013); Waldmann et al (2015); Line et al (2016).…”
Section: Nested Sampling Retrievalmentioning
confidence: 99%
“…To begin, N live points are randomly selected from the parameter space under the constrain of our prior, where N parameters form an N-dimensional parameter space. For this first set of points, the likelihood values are calculated and in each step, the algorithm discards the point with the lowest likelihood and adds a new one until convergence is reached (see Skilling (2006); Lavie et al (2017)). Taking into account the independent Gaussian errors of our spectral observations, we calculate the likelihood as a Gaussian function (Equation 3 in (Benneke & Seager 2013 (Feroz & Hobson 2008;Feroz et al 2009Feroz et al , 2013.…”
Section: Nested Sampling Retrievalmentioning
confidence: 99%
“…We use the popular MultiNest (Feroz & Hobson 2008;Feroz et al 2009) implementation of nested sampling through the PyMultiNest (Buchner et al 2014) Python inter-face. This has been widely used in the past to perform model selection for astrophysical spectra (e.g., Bernardi et al 2016;Feldmeier-Krause et al 2017;Lavie et al 2017;Baronchelli et al 2018). The likelihood function was sampled through a wrapper to pyspeckit, specifically designed to perform nested sampling of spectral cubes 1 .…”
Section: Bayesian Frameworkmentioning
confidence: 99%