2020PASP..132d4502J

Future space telescopes now in the concept and design stage aim to observe reflected light spectra of extrasolar planets. Assessing whether given notional mission and instrument design parameters will provide data suitable for constraining quantities of interest typically requires time consuming retrieval studies in which tens to hundreds of thousands of models are compared to data with a given assumed signal to noise ratio, thereby limiting the rapidity of design iterations. Here we present a machine learning approach employing a Multilayer Perceptron (MLP) trained on model albedo spectra of extrasolar giant planets to estimate a planet's atmospheric metallicity, gravity, effective temperature, and cloud properties given simulated observed spectra. The stand-alone C++ code we have developed can train new MLP's on new training sets within minutes to hours, depending upon the dimensions of input spectra, size of the training set, desired output, and desired accuracy. After the MLP is trained, it can classify new input spectra within a second, potentially helping speed observation and mission design planning. Our MLP's were trained using a grid of model spectra that varied in metallicity, gravity, temperature, and cloud properties. The results show that a trained MLP is an elegant means for reliable in situ estimations when applied to model spectra. We analyzed the effect of using models in a grid range known to have degeneracies.