Rescaling merger trees with a halo-profile correction enables cheap generation of galaxy summary statistics across cosmologies using semi-analytic models, matching dedicated simulation accuracy with far fewer base runs.
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Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
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Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models
Rescaling merger trees with a halo-profile correction enables cheap generation of galaxy summary statistics across cosmologies using semi-analytic models, matching dedicated simulation accuracy with far fewer base runs.
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.