Conference Reports
Unsupervised Domain Adaptation for Constraining Star Formation Histories
Astronomy 2024, 3(3), 189-207; https://doi.org/10.3390/astronomy3030012
In astronomy, understanding the evolutionary trajectories of galaxies necessitates a robust analysis of their star formation histories (SFHs), a task complicated by our inability to observe these vast celestial entities throughout their billion-year lifespans. This study pioneers the application of the Kullback–Leibler Importance Estimation Procedure (KLIEP), an unsupervised domain adaptation technique, to address this challenge. By adeptly applying KLIEP, we harness the power of machine learning to innovatively predict SFHs, utilizing simulated galaxy models to forge a novel linkage between simulation and observation. This methodology signifies a substantial advancement beyond the traditional Bayesian approaches to Spectral Energy Distribution (SED) analysis, which are often undermined by the absence of empirical SFH benchmarks. Our empirical investigations reveal that KLIEP markedly enhances the precision and reliability of SFH inference, offering a significant leap forward compared to existing methodologies. The results underscore the potential of KLIEP in refining our comprehension of galactic evolution, paving the way for its application in analyzing actual astronomical observations. Accompanying this paper, we provide access to the supporting code and dataset on GitHub, encouraging further exploration and validation of the efficacy of the KLIEP in the field.
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Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions
Astronomy 2024, 3(1), 14-20; https://doi.org/10.3390/astronomy3010002
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 scikit-learn-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 and without conformal prediction, demonstrating improved performance using metrics such as coverage and interval width. Our method offers a more versatile and accurate tool for deriving galaxy physical properties from observational data.
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