  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 novel machine learning approach employing
five Multilayer Perceptron's (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 and desired output. After the MLP is trained, it can classify new input
spectra within a second, potentially helping speed observation and mission
design planning. Four of the MLP's were tuned to work with various levels of
spectral noise. The fifth MLP was developed for cases where the user is
uncertain about the noise level. The MLP's were trained using a grid of model
spectra that varied in metallicity, gravity, temperature, and cloud properties.
We tested the MLP on noisy models and observed spectra of both Jupiter and
Saturn. The root mean squared error when applied to noisy models were on the
order of the model grid intervals. The results show that a trained MLP is a
robust means for reliable in-situ estimations, providing an elegant artificial
intelligence that is simple to customize and quick to use.
