Quanz, Bernhard SchölkopfĬomments: Accepted for publication in Astronomy & Astrophysics By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps P to T. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. A key component in simulating spectra is the pressure-temperature (PT) profile, which describes the thermal structure of the atmosphere.Ĭurrent AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters.
2008 SPECTRA SIMULATOR
astro-ph.IMĪtmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. Right: The relative fitting error of the spectrum, computed as (true spectrum − simulated spectrum) / true spectrum. Left: The retrieved PT profiles for the polynomial baseline and our model, together with the emission contribution function. Results for our simulated atmospheric retrieval of an Earth-like planet using petitRADTRANS, LIFEsim, and PyMultiNest.
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