A new analytical formalism self-consistently predicts both the ionized fraction x_i(z) and photoionization rate Gamma_HI(z), achieving percent-level accuracy in x_i and 20-30% accuracy in Gamma_HI versus radiative transfer simulations at z less than or equal to 6.
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UNVERDICTED 4representative citing papers
New JWST pure-parallel imaging over 400 arcmin² yields UV luminosity functions at z~7.5-10 consistent with pre-JWST models and significant clustering of bright galaxies implying they occupy more massive halos than previously modeled.
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.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
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A self-consistent analytical model for both the photoionization rate and reionization history
A new analytical formalism self-consistently predicts both the ionized fraction x_i(z) and photoionization rate Gamma_HI(z), achieving percent-level accuracy in x_i and 20-30% accuracy in Gamma_HI versus radiative transfer simulations at z less than or equal to 6.
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BEACON: JWST NIRCam Pure-parallel Imaging Survey. III. Constraints on the UV LF and the Clustering of z~7-14 Galaxies
New JWST pure-parallel imaging over 400 arcmin² yields UV luminosity functions at z~7.5-10 consistent with pre-JWST models and significant clustering of bright galaxies implying they occupy more massive halos than previously modeled.
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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.