A variational autoencoder learns to generate and reconstruct quasar spectra from SDSS data, reproducing median and variance properties while enabling photometry synthesis and absorption-line interpolation without ad-hoc tuning.
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Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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QUEST (Quasar Unsupervised Encoder and Synthesis Tool): A machine learning framework to generate quasar spectra
A variational autoencoder learns to generate and reconstruct quasar spectra from SDSS data, reproducing median and variance properties while enabling photometry synthesis and absorption-line interpolation without ad-hoc tuning.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.