Kavli Affiliate: Bruce Macintosh
| First 5 Authors: Simon Petrus, Niall Whiteford, Polychronis Patapis, Beth A. Biller, Andrew Skemer
| Summary:
The unprecedented medium-resolution (R=1500-3500) near- and mid-infrared
(1-18um) spectrum provided by JWST for the young (120-160Myr) low-mass
(12-20MJup) L-T transition (L7) companion VHS1256b has opened new avenues for
the in-depth characterization of substellar object atmospheres. In this study,
we present a comprehensive analysis of this dataset utilizing a forward
modelling approach. We explore five distinct atmospheric models, each aiming to
encompass diverse physical and chemical phenomena proposed to happen in cool
atmospheres in a self-consistent way. Our aim is to assess their performance in
estimating key atmospheric parameters: Teff, log(g), [M/H], C/O, gamma, fsed,
and R. To achieve this, we apply our Bayesian framework, ForMoSA, employing an
optimized nested sampling algorithm for model-data comparison. Our findings
reveal that each parameter’s estimate is significantly influenced by factors
such as the wavelength range considered, the spectral features used, the
signal-to-noise ratio, and the model chosen for the fit. The observed parameter
dispersion may be attributed to systematic error in the models, resulting from
their difficulties in accurately replicating the complex atmospheric structure
of VHS1256b, notably the complexity of its clouds and dust distribution. To
propagate the impact of these systematic uncertainties on our atmospheric
property estimates, we introduce innovative fitting methodologies based on
independent fits performed on different spectral windows. We finally derived a
Teff consistent with the spectral type of the target, considering its young
age, which is confirmed by our estimate of log(g). Despite the exceptional data
quality, attaining robust estimates for chemical abundances [M/H] and C/O,
often employed as indicators of formation history, remains challenging with
estimates dispersed on the entire range explored by the model grids.
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