Presentation #119.03 in the session “Machine Learning in Astronomy: Measuring the Properties of Galaxies with Machine Learning (Meeting-in-a-Meeting)”.
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 of an ensemble of supervised machine learning-based models capable of non-linearly 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 (SHAP) to fairly evaluate the relative importance of different bands to understand why particular predictions were reached. Finally, we present preliminary results when mirkwood is applied to 7,000 nearby galaxies from the GAMA survey. 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.