A new overdensity-conditioned emulator trained on small subvolumes from Quijote recovers the global halo mass function via integration over the overdensity distribution at 0.026% of the simulation cost.
C., et al., 2024, @doi [arXiv] 10.48550/arXiv.2411.13960
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AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
A new overdensity-conditioned emulator trained on small subvolumes from Quijote recovers the global halo mass function via integration over the overdensity distribution at 0.026% of the simulation cost.
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Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.