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LUCI is an general-purpose spectral line-fitting pipeline which nativelyintegrates machine learning algorithms to initialize fit functions. LUCIcurrently uses point-estimates obtained from a convolutional neural network(CNN) to inform optimization algorithms; this methodology has shown greatpromise by reducing computation time and reducing the chance of falling into alocal minimum using convex optimization methods. In this update to LUCI, weexpand upon the CNN developed in Rhea et al. 2020 so that it outputs Gaussianposterior distributions of the fit parameters of interest (the velocity andbroadening) rather than simple point-estimates. Moreover, these posteriors arethen used to inform the priors in a Bayesian inference scheme, either emcee ordynesty. The code is publicly available at https://github.com/crhea93/LUCI.

Updates to LUCI: A New Fitting Paradigm Using Mixture Density Networks