Machine Learning Retrieval of Jovian and Terrestrial Atmospheres

Machine learning approaches to atmospheric retrieval offer results comparable to traditional numerical approaches in just seconds, compared to hundreds of compute hours. This opens the possibility for fully-3D retrievals to execute in times comparable to traditional approaches. Recently, we developed plan-net, an ensemble of Bayesian neural networks for atmospheric retrieval; we trained plan-net on synthetic Wide Field Camera 3 (WFC3) hot-Jupiter transmission spectra, applied it to the WFC3 spectrum of WASP-12b, and found results consistent with the literature. Here, we present updates to plan-net and expand its application to our 28-parameter data set of simulated LUVOIR spectra of terrestrial exoplanets generated using the NASA Planetary Spectrum Generator. By including both dense dropout and convolutional layers, we find a significant improvement in accuracy. MH and FS acknowledge the support of NVIDIA Corporation for the donation of the Titan Xp GPUs used for this research. AC is sponsored by the AIMS-CDT and EPSRC. AGB is funded by Lawrence Berkeley National Lab and EPSRC/MURI grant EP/N019474/1.