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
On Separate Universes
3 Pith papers cite this work. Polarity classification is still indexing.
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Conditioning CAMELS-SAM simulations on the stellar mass function or stellar-to-halo mass relation reduces uncertainty in b_phi by 88-97% for DESI emission line galaxy samples while remaining consistent across galaxy formation variations.
Non-conserved biased tracers debias more rapidly than conserved tracers, leading to time-dependent suppression of large-scale power.
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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Informative Priors on Primordial Non-Gaussianity Bias $b_{\phi}$ From Galaxy Formation
Conditioning CAMELS-SAM simulations on the stellar mass function or stellar-to-halo mass relation reduces uncertainty in b_phi by 88-97% for DESI emission line galaxy samples while remaining consistent across galaxy formation variations.
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Non-conservation and time non-locality of biased tracers
Non-conserved biased tracers debias more rapidly than conserved tracers, leading to time-dependent suppression of large-scale power.