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
The Effective Field Theory of Inflation Models with Sharp Features
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Updated Planck CMB measurements give ns = 0.9649 ± 0.0042, r < 0.056, confirm flatness at 0.4 percent, and show no evidence for scale-dependent features or non-slow-roll dynamics in the inflaton potential.
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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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Planck 2018 results. X. Constraints on inflation
Updated Planck CMB measurements give ns = 0.9649 ± 0.0042, r < 0.056, confirm flatness at 0.4 percent, and show no evidence for scale-dependent features or non-slow-roll dynamics in the inflaton potential.