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Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference

MH Kanafi, SMS Movahed - arXiv preprint arXiv:2511.03636, 2025 - arxiv.org
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In this work, we perform a simulation-based forecasting analysis to compare the constraining power of two higher-order summary statistics of the large-scale structure (LSS)…

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@article{2511.03636v1,
Author        = {M. H. Jalali Kanafi and S. M. S. Movahed},
Title         = {Quantifying Weighted Morphological Content of Large-Scale Structures via
  Simulation-Based Inference},
Eprint        = {2511.03636v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {In this work, we perform a simulation-based forecasting analysis to compare
the constraining power of two higher-order summary statistics of the
large-scale structure (LSS), the Minkowski Functionals (MFs) and the
Conditional Moments of Derivative (CMD), with a particular focus on their
sensitivity to nonlinear and anisotropic features in redshift-space. Our
analysis relies on halo catalogs from the Big Sobol Sequence(BSQ) simulations
at redshift $z=0.5$, employing a likelihood-free inference framework
implemented via neural posterior estimation. At the fiducial cosmology of the
Quijote simulations $(\Omega_{m}=0.3175,\,\sigma_{8}=0.834)$, and for the
smoothing scale $R=15\,h^{-1}$Mpc, we find that the CMD yields tighter
forecasts for $(\Omega_{m}},\,\sigma_{8})$ than the zeroth- to third-order MFs
components, improving the constraint precision by ${\sim}(44\%,\,52\%)$,
${\sim}(30\%,\,45\%)$, ${\sim}(27\%,\,17\%)$, and ${\sim}(26\%,\,17\%)$,
respectively. A joint configuration combining the MFs and CMD further enhances
the precision by approximately ${\sim}27\%$ compared to the standard MFs alone,
highlighting the complementary anisotropy-sensitive information captured by the
CMD in contrast to the scalar morphological content encapsulated by the MFs. We
further extend the forecasting analysis to a continuous range of cosmological
parameter values and multiple smoothing scales. Our results show that, although
the absolute forecast uncertainty for each component of summary statistics
depends on the underlying parameter values and the adopted smoothing scale, the
relative constraining power among the summary statistics remains nearly
constant throughout.},
Year          = {2025},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2511.03636v1},
File          = {2511.03636v1.pdf}
}

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