Unsupervised Domain Adaptation for Constraining Star Formation Histories

Gilda, Sankalp; Mathelin, Antoine de; Bellstedt, Sabine; Richard, Guillaume

doi:10.48550/arxiv.2112.14072

Cited by 1 publication

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“…Recent advancements in computational methods have opened new avenues in this field. Machine learning (ML), with its ability to handle large datasets and uncover complex patterns, has emerged as a powerful tool in SED fitting [1,2,7,8]. The traditional parametric and often linear approaches are being supplemented, and in some cases replaced, by nonparametric, highly flexible ML techniques that can model the non-linear relationships intrinsic to astronomical data more effectively [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions

Gilda

2024

Astronomy

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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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