The splashback radius of optically selected clusters with Subaru HSC
  Second Public Data Release
  Recent constraints on the splashback radius around optically selected galaxy
clusters from the redMaPPer cluster-finding algorithm in the literature have
shown that the observed splashback radius is $\sim 20\%$ smaller than that
predicted by N-body simulations. We present analyses on the splashback features
around $\sim 3000$ optically selected galaxy clusters detected by the
independent cluster-finding algorithm CAMIRA over a wide redshift range of
$0.1<z_{\rm cl}<1.0$ from the second public data release of the Hyper
Suprime-Cam (HSC) Subaru Strategic Program covering $\sim 427~{\rm deg}^2$ for
the cluster catalog. We detect the splashback feature from the projected
cross-correlation measurements between the clusters and photometric galaxies
over the wide redshift range, including for high redshift clusters at
$0.7<z_{\rm cl}<1.0$, thanks to deep HSC images. We find that constraints from
red galaxy populations only are more precise than those without any color cut,
leading to $1\sigma$ precisions of $\sim 15\%$ at $0.4<z_{\rm cl}<0.7$ and
$0.7<z_{\rm cl}<1.0$. These constraints are more consistent with the model
predictions ($\lesssim 1\sigma$) than their $20\%$ smaller values as suggested
by the previous studies with the redMaPPer ($\sim 2\sigma$). We also
investigate selection effects of the optical cluster-finding algorithms on the
observed splashback features by creating mock galaxy catalogs from a halo
occupation distribution model, and find that such effects to be sub-dominant
for the CAMIRA cluster-finding algorithm. We also find that the redMaPPer-like
cluster-finding algorithm induces a smaller inferred splashback radius in our
mock catalog, especially at lower richness, which can well explain the smaller
splashback radii in the literature. In contrast, these biases are significantly
reduced when increasing its aperture size.
