DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
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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.
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
skysurvey provides a Python framework with Target, Survey, and DataSet classes plus modeldag to simulate transient sky observations, demonstrated on Type Ia supernovae populations and ZTF DR2 replication.
New CAMELS simulations in larger (50 Mpc/h)^3 boxes with 35 varied parameters produce tighter neural-network constraints on model parameters than prior smaller-volume runs, with public data release.
citing papers explorer
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Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
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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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Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
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skysurvey: a pure python package to simulate the transient sky
skysurvey provides a Python framework with Target, Survey, and DataSet classes plus modeldag to simulate transient sky observations, demonstrated on Type Ia supernovae populations and ZTF DR2 replication.
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Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
New CAMELS simulations in larger (50 Mpc/h)^3 boxes with 35 varied parameters produce tighter neural-network constraints on model parameters than prior smaller-volume runs, with public data release.