posterior-landscape: morphological feature finding in two-dimensional probability densities
Abstract
We present posterior-landscape, a Python framework to identify and characterize morphological features in two-dimensional probability densities. The framework combines concepts from topological data analysis, statistical density-ridge estimation, and scale-space image analysis. It extracts localized concentrations, valleys, ridges, and changes of slope; for Bayesian reconstructions, it propagates uncertainty in feature recovery, geometry, and correspondence across posteriors. The framework also connects two-dimensional structures to features in one-dimensional marginals, and quantifies their correlation with other inferred parameters. We validate the method on controlled densities, and demonstrate its use on gravitational-wave population reconstructions, where it provides a systematic way to identify and interpret population features and assess which ones carry information relevant for cosmology. The resulting catalogues also offer a common morphological representation for future comparisons between predictions and population reconstructions.
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BibTeX
@misc{mancarella2026posteriorlandscape,
title = {{posterior-landscape: morphological feature finding in two-dimensional probability densities}},
author = {Michele Mancarella and Viola De Renzis and Alessandro Agapito},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40272},
eprint = {2610.40272},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40272},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - posterior-landscape: morphological feature finding in two-dimensional probability densities
AU - Michele Mancarella
AU - Viola De Renzis
AU - Alessandro Agapito
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40272
UR - https://arxiv.org/abs/2610.40272
AB - We present posterior-landscape, a Python framework to identify and characterize morphological features in two-dimensional probability densities. The framework combines concepts from topological data analysis, statistical density-ridge estimation, and scale-space image analysis. It extracts localized concentrations, valleys, ridges, and changes of slope; for Bayesian reconstructions, it propagates uncertainty in feature recovery, geometry, and correspondence across posteriors. The framework also connects two-dimensional structures to features in one-dimensional marginals, and quantifies their correlation with other inferred parameters. We validate the method on controlled densities, and demonstrate its use on gravitational-wave population reconstructions, where it provides a systematic way to identify and interpret population features and assess which ones carry information relevant for cosmology. The resulting catalogues also offer a common morphological representation for future comparisons between predictions and population reconstructions.
ER -