Abstract
Abstract We super-resolve the seeing-limited Subaru Hyper Suprime-Cam (HSC) images for 32187 galaxies at $z\sim 2$–5 using three techniques, namely, the classical Richardson–Lucy (RL) point spread function (PSF) deconvolution, sparse modeling, and generative adversarial networks, to investigate the environmental dependence of galaxy mergers. These three techniques generate overall similar high spatial resolution images but with some slight differences in galaxy structures; for example, more residual noises are seen in the classical RL PSF deconvolution. To alleviate the disadvantages of each technique, we create combined images by averaging over the three types of super-resolution images, resulting in galaxy substructures resembling those seen in the Hubble Space Telescope images. Using the combined super-resolution images, we measure the relative galaxy major merger fraction corrected for the chance projection effect, $f_{\rm merger}^{\rm rel,col}$, for galaxies in the $\sim$300 deg$^2$ area data of the HSC Strategic Survey Program and the CFHT Large Area U-band Survey. Our $f_{\rm merger}^{\rm rel,col}$ measurements at $z\sim 3$ validate previous findings showing that $f_{\rm merger}^{\rm rel,col}$ is higher in regions with a higher galaxy overdensity $\delta$ at $z\sim 2$–3. Thanks to the large galaxy sample, we identify a nearly linear increase in $f_{\rm merger}^{\rm rel,col}$ with increasing $\delta$ at $z\sim 4$–5, providing the highest-z observational evidence that galaxy mergers are related to $\delta$. In addition to our $f_{\rm merger}^{\rm rel,col}$ measurements, we find that the galaxy merger fractions in the literature also broadly align with the linear $f_{\rm merger}^{\rm rel,col}$–$\delta$ relation across a wide redshift range of $z\sim 2$–5. This alignment suggests that the linear $f_{\rm merger}^{\rm rel,col}$–$\delta$ relation can serve as a valuable tool for quantitatively estimating the contributions of galaxy mergers to various environmental dependences. This super-resolution analysis can be readily applied to datasets from wide field-of-view space telescopes such as Euclid and Roman.
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