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Cosmology from HSC Y1 Weak Lensing with Combined Higher-Order Statistics and Simulation-based Inference

CP Novaes, L Thiele, J Armijo, S Cheng… - arXiv preprint arXiv …, 2024 - arxiv.org
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… Here, instead, we forward model the summary statistics of HSC Y1 and perform a simulationbased inference (SBI) [17], also known as likelihood-free or implicit-likelihood …

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@article{2409.01301v1,
Author        = {Camila P. Novaes and Leander Thiele and Joaquin Armijo and Sihao Cheng and Jessica A. Cowell and Gabriela A. Marques and Elisa G. M. Ferreira and Masato Shirasaki and Ken Osato and Jia Liu},
Title         = {Cosmology from HSC Y1 Weak Lensing with Combined Higher-Order Statistics
  and Simulation-based Inference},
Eprint        = {2409.01301v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present cosmological constraints from weak lensing with the Subaru Hyper
Suprime-Cam (HSC) first-year (Y1) data, using a simulation-based inference
(SBI) method. % We explore the performance of a set of higher-order statistics
(HOS) including the Minkowski functionals, counts of peaks and minima, and the
probability distribution function and compare them to the traditional two-point
statistics. The HOS, also known as non-Gaussian statistics, can extract
additional non-Gaussian information that is inaccessible to the two-point
statistics. We use a neural network to compress the summary statistics,
followed by an SBI approach to infer the posterior distribution of the
cosmological parameters. We apply cuts on angular scales and redshift bins to
mitigate the impact of systematic effects. Combining two-point and non-Gaussian
statistics, we obtain $S_8 \equiv \sigma_8 \sqrt{\Omega_m/0.3} =
0.804_{-0.040}^{+0.041}$ and $\Omega_m = 0.344_{-0.090}^{+0.083}$, similar to
that from non-Gaussian statistics alone. These results are consistent with
previous HSC analyses and Planck 2018 cosmology. Our constraints from
non-Gaussian statistics are $\sim 25\%$ tighter in $S_8$ than two-point
statistics, where the main improvement lies in $\Omega_m$, with $\sim 40$\%
tighter error bar compared to using the angular power spectrum alone ($S_8 =
0.766_{-0.056}^{+0.054}$ and $\Omega_m = 0.365_{-0.141}^{+0.148}$). We find
that, among the non-Gaussian statistics we studied, the Minkowski functionals
are the primary driver for this improvement. Our analyses confirm the SBI as a
powerful approach for cosmological constraints, avoiding any assumptions about
the functional form of the data's likelihood.},
Year          = {2024},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2409.01301v1},
File          = {2409.01301v1.pdf}
}

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