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
CMB polarization from secondary vector and tensor modes
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2representative citing papers
Explores SKAO detection of scalar-induced GW backgrounds as probes of primordial non-Gaussianity and parity violation, with LSS cross-correlation to improve SNR.
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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Exploring Gravitational Wave Signatures Due to Primordial Non-gaussianity and Large Scale Structure Using SKAO
Explores SKAO detection of scalar-induced GW backgrounds as probes of primordial non-Gaussianity and parity violation, with LSS cross-correlation to improve SNR.