Presents a fully photometric framework to measure individual cluster splashback radii and masses from SDSS data and constructs the first observational splashback mass function.
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@doi [ ] 10.1046/j.1365-8711.2001.04416.x, http://adsabs.harvard.edu/abs/2001MNRAS.325..231O 325
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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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Inverting Fisher biases enables quick checks on whether specific systematics explain cosmological tensions.
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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