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Cosmological constraints from Subaru weak lensing cluster counts

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abstract

We present results of weak lensing cluster counts obtained from 11 sq.deg SuprimeCam data. Although the area is much smaller than previous work dealing with weak lensing peak statistics, the number density of galaxies usable for weak lensing analysis is about twice as large as those. The higher galaxy number density reduces the noise in the weak lensing mass maps, and thus increases the signal-to-noise ratio of peaks of the lensing signal due to massive clusters. This enables us to construct a weak lensing selected cluster sample by adopting a high threshold S/N, such that the contamination rate due to false signals is small. We find 6 peaks with S/N>5. For all the peaks, previously identified clusters of galaxies are matched within a separation of 1 arcmin, demonstrating good correspondence between the peaks and clusters of galaxies. We evaluate the statistical error using mock weak lensing data, and find Npeak=6+/-3.1 in an effective area of 9.0 sq.deg. We compare the measured weak lensing cluster counts with the theoretical model prediction based on halo models and place the constraint on Omega_m-sigma_8 plane which is found to be consistent with currently standard LCDM models. It is demonstrated that the weak lensing cluster counts can place a unique constraint on sigma_8-c_0 plane, where c_0 is the normalization of the dark matter halo mass-concentration relationship. Finally we discuss prospects for ongoing/future wide field optical galaxy surveys.

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astro-ph.CO 1

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2026 1

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representative citing papers

Machine-learning applications for weak-lensing cosmology

astro-ph.CO · 2026-05-13 · unverdicted · novelty 2.0

Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.

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  • Machine-learning applications for weak-lensing cosmology astro-ph.CO · 2026-05-13 · unverdicted · none · ref 24 · internal anchor

    Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.