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Building Trustworthy Machine Learning Models for Astronomy – on November 29, 2021 at 6:50 am
Astronomy is entering an era of data-driven discovery, due in part to modern machine learning (ML) techniques enabling powerful new ways to interpret observations. This shift in our scientific approach requires us to consider whether we can trust the black box. Here, we overview methods for an often-overlooked step in the development of ML models:…
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Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z – on November 23, 2021 at 11:25 am
Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Motivated by these considerations, we propose using physically motivated synthetic spectral energy distributions in redshift estimation. In addition, the synthetic data would have…
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Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z – on November 23, 2021 at 11:25 am
Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Motivated by these considerations, we propose using physically motivated synthetic spectral energy distributions in redshift estimation. In addition, the synthetic data would have…