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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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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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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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.