TopoFisher optimizes trainable filtrations, vectorizations, and compressors in persistent homology to maximize Fisher information, yielding higher information than fixed cosmological summaries and approaching neural baselines with far fewer parameters while generalizing better under simulator shifts
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A neural marking scheme trained with contrastive learning tightens constraints on σ8 by 2.9× and Ωm by 1.8× over classical marks at k_max=0.2 h/Mpc while breaking their degeneracy at the Fisher level.
Re-expressing the Hubble tension via posterior-implied E(z) histories yields moderate mismatches (S_hist of 1.65 and 2.55) that correspond to only 1.1-2.1 sigma equivalents, below the usual 4.9 sigma scalar-H0 discrepancy.
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.
Forecasts show SKA-Mid cross-correlations with ET/CE gravitational wave events can constrain GW source bias and time-delay distributions.
citing papers explorer
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TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information
TopoFisher optimizes trainable filtrations, vectorizations, and compressors in persistent homology to maximize Fisher information, yielding higher information than fixed cosmological summaries and approaching neural baselines with far fewer parameters while generalizing better under simulator shifts
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Interpretable Neural Marked Statistics for Cosmological Inference
A neural marking scheme trained with contrastive learning tightens constraints on σ8 by 2.9× and Ωm by 1.8× over classical marks at k_max=0.2 h/Mpc while breaking their degeneracy at the Fisher level.
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From Scalar $H_0$ to $E(z)$: A Reformulation of the Hubble Tension
Re-expressing the Hubble tension via posterior-implied E(z) histories yields moderate mismatches (S_hist of 1.65 and 2.55) that correspond to only 1.1-2.1 sigma equivalents, below the usual 4.9 sigma scalar-H0 discrepancy.
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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.
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Using SKAO to Understand the Clustering of Gravitational Wave Sources
Forecasts show SKA-Mid cross-correlations with ET/CE gravitational wave events can constrain GW source bias and time-delay distributions.