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
Generation and Characterization of Large Non-Gaussianities in Single Field Inflation
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Single-field slow-roll inflation achieves ns=1 by ending inflation suddenly via a large step in the potential.
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.
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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Single field slow-roll inflation with step uplift to $n_s=1$
Single-field slow-roll inflation achieves ns=1 by ending inflation suddenly via a large step in the potential.
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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.