Constraining Methane Abundance and Cloud Properties from the Reflected Light Spectra of Directly Imaged Exoplanets

Abstract:

We have assembled an atmospheric retrieval package for the reflected light spectra of gas- and ice- giants in order to inform the design and estimate the scientific return of future space-based coronagraph instruments. Such instruments will have a working bandpass of ~0.4-1 μm and a resolving power R~70, and will enable the characterization of tens of exoplanets in the Solar neighborhood. The targets will be chosen form known RV giants, with estimated effective temperatures of ~100-600 K and masses between 0.3 and 20 M_Jupiter. In this regime, both methane and clouds will have the largest effects on the observed spectra. Our retrieval code is the first to include cloud properties in the core set of parameters, along with methane abundance and surface gravity. We consider three possible cloud structure scenarios, with 0, 1 or 2 cloud layers, respectively. The best-fit parameters for a given model are determined using a Monte Carlo Markov Chain ensemble sampler, and the most favored cloud structure is chosen by calculating the Bayes factors between different models. We present the performance of our retrieval technique applied to a set of representative model spectra, covering a SNR range form 5 to 20 and including possible noise correlations over a 25 or 100 nanometer scale. Further, we apply the technique to more realistic cases, namely simulated observations of Jupiter, Saturn, Uranus, and the gas-giant HD99492c. In each case, we determine the confidence levels associated with the methane and cloud detections, as a function of SNR and noise properties.