Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions

Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves. We propose an advanced machine learning-based approach that enhances flexibility and uncertainty quantification in SED fitting. Unlike the fixed NGBoost model used in mirkwood, our approach allows for any sklearn-compatible model, including deterministic models. We incorporate conformalized quantile regression to convert point predictions into error bars, enhancing interpretability and reliability. Using CatBoost as the base predictor, we compare results with …

advanced assumptions astro-ph.ga astro-ph.im beyond cs.lg distribution dust energy face flexibility machine machine learning modeling predictions quantification sklearn star uncertainty