MIRKWOOD: Fast and Accurate SED Modeling Using Machine Learning

Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves. Beyond this, Bayesian fitting (which is typically used in SED fitting software) is an intrinsically compute-intensive task, often requiring access to expensive hardware for long periods of time. To overcome these shortcomings, we have developed MIRKWOOD: a user-friendly tool comprising an ensemble of supervised machine-learning-based models capable of nonlinearly mapping galaxy fluxes to their properties. By stacking multiple models, we marginalize against any individual model's poor performance in a given region of the parameter space. We demonstrate MIRKWOOD's significantly improved performance over traditional techniques by training it on a combined data set of mock photometry of z = 0 galaxies from the SIMBA, EAGLE, and ILLUSTRISTNG cosmological simulations, and comparing the derived results with those obtained from traditional SED fitting techniques. MIRKWOOD is also able to account for uncertainties arising both from intrinsic noise in observations, and from finite training data and incorrect modeling assumptions. To increase the added value to the observational community, we use Shapley value explanations to fairly evaluate the relative importance of different bands to understand why particular predictions were reached. We envisage MIRKWOOD to be an evolving, open-source framework that will provide highly accurate physical properties from observations of galaxies as compared to traditional SED fitting.