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Cosmological inference from emulator based halo model I: Validation tests with HSC and SDSS mock catalogs

31 Dec 2020arXiv:2101.00113links table onlyarchive 2025-07-28

Hironao Miyatake, Yosuke Kobayashi, Masahiro Takada, Takahiro Nishimichi, Masato Shirasaki, Sunao Sugiyama, Ryuichi Takahashi, Ken Osato, Surhud More, Youngsoo Park

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We present validation tests of emulator-based halo model method for cosmological parameter inference, assuming hypothetical measurements of the projected correlation function of galaxies, wₚ(R), and the galaxy-galaxy weak lensing, ΔΣ(R), from the spectroscopic SDSS galaxies and the Hyper Suprime-Cam Year1 (HSC-Y1) galaxies. To do this, we use \textsc{Dark Emulator} developed in Nishimichi et al. based on an ensemble of N-body simulations, which is an emulation package enabling a fast, accurate computation of halo clustering quantities for flat-geometry $w$CDM cosmologies. Adopting the halo occupation distribution, the emulator allows us to obtain model predictions of ΔΣ and wₚ for the SDSS-like galaxies at a few CPU seconds for an input set of parameters. We present performance and validation of the method by carrying out Markov Chain Monte Carlo analyses of the mock signals measured from a variety of mock catalogs that mimic the SDSS and HSC-Y1 galaxies. We show that the halo model method can recover the underlying true cosmological parameters to within the 68\% credible interval, except for the mocks including the assembly bias effect (although we consider the unrealistically-large amplitude of assembly bias effect). Even for the assembly bias mock, we demonstrate that the cosmological parameters can be recovered {\it if} the analysis is restricted to scales R≳10 h⁻¹Mpc. We also show that, by using a single population of source galaxies to infer the relative strengths of ΔΣ for multiple lens samples at different redshifts, the joint probes method allows for self-calibration of photometric redshift errors and multiplicative shear bias. Thus we conclude that the emulator-based halo model method can be safely applied to the HSC-Y1 dataset, achieving a precision of σ(S₈)≃0.04.

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