Table 1.

Sample selections for the HSC CAMIRA clusters and their characteristics.*

Selection
CharacteristicFullLow-zMid-zHigh-zLow-NMid-NHigh-N
z  cl, min0.10.10.40.70.10.10.1
z  cl, max1.00.40.71.01.01.01.0
zcl0.570.260.550.830.600.540.49
N  min15151515152030
N  max2002002002002030200
N23252221172440
Number of clusters33169681140120817741067475
M200m〉 [h−11014M]|$1.72^{+0.08}_{-0.07}$||$1.71^{+0.07}_{-0.07}$||$1.93^{+0.09}_{-0.08}$||$1.51^{+0.13}_{-0.13}$||$1.23^{+0.06}_{-0.06}$||$1.78^{+0.08}_{-0.08}$||$3.32^{+0.14}_{-0.15}$|
R200m〉 [h−1 Mpc]|$1.33^{+0.02}_{-0.02}$||$1.33^{+0.02}_{-0.02}$||$1.38^{+0.02}_{-0.02}$||$1.27^{+0.04}_{-0.04}$||$1.19^{+0.02}_{-0.02}$||$1.34^{+0.02}_{-0.02}$||$1.65^{+0.02}_{-0.03}$|
|$r_{\rm sp, model}^{\rm 3D}\ [h^{-1}\:\mbox{Mpc}]$||$1.61^{+0.01}_{-0.01}$||$1.61^{+0.01}_{-0.01}$||$1.63^{+0.01}_{-0.01}$||$1.45^{+0.03}_{-0.02}$||$1.41^{+0.01}_{-0.01}$||$1.62^{+0.01}_{-0.01}$||$1.92^{+0.01}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 3D} }{ {d}\ln r }|_{r=r_{\rm sp}^{\rm 3D}, {\rm model}}$||$-3.20^{+0.03}_{-0.03}$||$-3.11^{+0.03}_{-0.03}$||$-3.25^{+0.03}_{-0.03}$||$-3.27^{+0.05}_{-0.05}$||$-3.21^{+0.04}_{-0.04}$||$-3.28^{+0.03}_{-0.03}$||$-3.34^{+0.03}_{-0.03}$|
|$R_{\rm sp, model}^{\rm 2D}\ [h^{-1}\:\mbox{Mpc}]$||$1.12^{+0.01}_{-0.02}$||$1.07^{+0.03}_{-0.02}$||$1.16^{+0.01}_{-0.01}$||$1.07^{+0.04}_{-0.03}$||$0.99^{+0.01}_{-0.01}$||$1.13^{+0.01}_{-0.02}$||$1.39^{+0.02}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 2D} }{ {d}\ln R }|_{R=R_{\rm sp}^{\rm 2D}, {\rm model}}$||$-1.72^{+0.02}_{-0.02}$||$-1.67^{+0.01}_{-0.01}$||$-1.75^{+0.01}_{-0.01}$||$-1.75^{+0.03}_{-0.03}$||$-1.69^{+0.02}_{-0.02}$||$-1.75^{+0.02}_{-0.02}$||$-1.82^{+0.01}_{-0.01}$|
Selection
CharacteristicFullLow-zMid-zHigh-zLow-NMid-NHigh-N
z  cl, min0.10.10.40.70.10.10.1
z  cl, max1.00.40.71.01.01.01.0
zcl0.570.260.550.830.600.540.49
N  min15151515152030
N  max2002002002002030200
N23252221172440
Number of clusters33169681140120817741067475
M200m〉 [h−11014M]|$1.72^{+0.08}_{-0.07}$||$1.71^{+0.07}_{-0.07}$||$1.93^{+0.09}_{-0.08}$||$1.51^{+0.13}_{-0.13}$||$1.23^{+0.06}_{-0.06}$||$1.78^{+0.08}_{-0.08}$||$3.32^{+0.14}_{-0.15}$|
R200m〉 [h−1 Mpc]|$1.33^{+0.02}_{-0.02}$||$1.33^{+0.02}_{-0.02}$||$1.38^{+0.02}_{-0.02}$||$1.27^{+0.04}_{-0.04}$||$1.19^{+0.02}_{-0.02}$||$1.34^{+0.02}_{-0.02}$||$1.65^{+0.02}_{-0.03}$|
|$r_{\rm sp, model}^{\rm 3D}\ [h^{-1}\:\mbox{Mpc}]$||$1.61^{+0.01}_{-0.01}$||$1.61^{+0.01}_{-0.01}$||$1.63^{+0.01}_{-0.01}$||$1.45^{+0.03}_{-0.02}$||$1.41^{+0.01}_{-0.01}$||$1.62^{+0.01}_{-0.01}$||$1.92^{+0.01}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 3D} }{ {d}\ln r }|_{r=r_{\rm sp}^{\rm 3D}, {\rm model}}$||$-3.20^{+0.03}_{-0.03}$||$-3.11^{+0.03}_{-0.03}$||$-3.25^{+0.03}_{-0.03}$||$-3.27^{+0.05}_{-0.05}$||$-3.21^{+0.04}_{-0.04}$||$-3.28^{+0.03}_{-0.03}$||$-3.34^{+0.03}_{-0.03}$|
|$R_{\rm sp, model}^{\rm 2D}\ [h^{-1}\:\mbox{Mpc}]$||$1.12^{+0.01}_{-0.02}$||$1.07^{+0.03}_{-0.02}$||$1.16^{+0.01}_{-0.01}$||$1.07^{+0.04}_{-0.03}$||$0.99^{+0.01}_{-0.01}$||$1.13^{+0.01}_{-0.02}$||$1.39^{+0.02}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 2D} }{ {d}\ln R }|_{R=R_{\rm sp}^{\rm 2D}, {\rm model}}$||$-1.72^{+0.02}_{-0.02}$||$-1.67^{+0.01}_{-0.01}$||$-1.75^{+0.01}_{-0.01}$||$-1.75^{+0.03}_{-0.03}$||$-1.69^{+0.02}_{-0.02}$||$-1.75^{+0.02}_{-0.02}$||$-1.82^{+0.01}_{-0.01}$|

*We define each cluster sample by zcl, min, zcl, max, Nmin, and Nmax, and 〈zcl〉 and 〈N〉 give the mean values of cluster redshift and richness. We show the constraints for each sample on mean mass and R200m values, and the model predictions for the splashback feature from the mass–richness relation in Murata et al. (2019) and the halo emulator in Nishimichi et al. (2019) as described in appendix  1. We note that we use the halo–matter cross-correlation function for these model predictions and we compare our model predictions with model calculation methods in the literature in appendix  2. We show the median and the 16th and 84th percentiles of the model predictions using the fiducial results of the mass–richness relation in Murata et al. (2019) with the Planck cosmology.

Table 1.

Sample selections for the HSC CAMIRA clusters and their characteristics.*

Selection
CharacteristicFullLow-zMid-zHigh-zLow-NMid-NHigh-N
z  cl, min0.10.10.40.70.10.10.1
z  cl, max1.00.40.71.01.01.01.0
zcl0.570.260.550.830.600.540.49
N  min15151515152030
N  max2002002002002030200
N23252221172440
Number of clusters33169681140120817741067475
M200m〉 [h−11014M]|$1.72^{+0.08}_{-0.07}$||$1.71^{+0.07}_{-0.07}$||$1.93^{+0.09}_{-0.08}$||$1.51^{+0.13}_{-0.13}$||$1.23^{+0.06}_{-0.06}$||$1.78^{+0.08}_{-0.08}$||$3.32^{+0.14}_{-0.15}$|
R200m〉 [h−1 Mpc]|$1.33^{+0.02}_{-0.02}$||$1.33^{+0.02}_{-0.02}$||$1.38^{+0.02}_{-0.02}$||$1.27^{+0.04}_{-0.04}$||$1.19^{+0.02}_{-0.02}$||$1.34^{+0.02}_{-0.02}$||$1.65^{+0.02}_{-0.03}$|
|$r_{\rm sp, model}^{\rm 3D}\ [h^{-1}\:\mbox{Mpc}]$||$1.61^{+0.01}_{-0.01}$||$1.61^{+0.01}_{-0.01}$||$1.63^{+0.01}_{-0.01}$||$1.45^{+0.03}_{-0.02}$||$1.41^{+0.01}_{-0.01}$||$1.62^{+0.01}_{-0.01}$||$1.92^{+0.01}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 3D} }{ {d}\ln r }|_{r=r_{\rm sp}^{\rm 3D}, {\rm model}}$||$-3.20^{+0.03}_{-0.03}$||$-3.11^{+0.03}_{-0.03}$||$-3.25^{+0.03}_{-0.03}$||$-3.27^{+0.05}_{-0.05}$||$-3.21^{+0.04}_{-0.04}$||$-3.28^{+0.03}_{-0.03}$||$-3.34^{+0.03}_{-0.03}$|
|$R_{\rm sp, model}^{\rm 2D}\ [h^{-1}\:\mbox{Mpc}]$||$1.12^{+0.01}_{-0.02}$||$1.07^{+0.03}_{-0.02}$||$1.16^{+0.01}_{-0.01}$||$1.07^{+0.04}_{-0.03}$||$0.99^{+0.01}_{-0.01}$||$1.13^{+0.01}_{-0.02}$||$1.39^{+0.02}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 2D} }{ {d}\ln R }|_{R=R_{\rm sp}^{\rm 2D}, {\rm model}}$||$-1.72^{+0.02}_{-0.02}$||$-1.67^{+0.01}_{-0.01}$||$-1.75^{+0.01}_{-0.01}$||$-1.75^{+0.03}_{-0.03}$||$-1.69^{+0.02}_{-0.02}$||$-1.75^{+0.02}_{-0.02}$||$-1.82^{+0.01}_{-0.01}$|
Selection
CharacteristicFullLow-zMid-zHigh-zLow-NMid-NHigh-N
z  cl, min0.10.10.40.70.10.10.1
z  cl, max1.00.40.71.01.01.01.0
zcl0.570.260.550.830.600.540.49
N  min15151515152030
N  max2002002002002030200
N23252221172440
Number of clusters33169681140120817741067475
M200m〉 [h−11014M]|$1.72^{+0.08}_{-0.07}$||$1.71^{+0.07}_{-0.07}$||$1.93^{+0.09}_{-0.08}$||$1.51^{+0.13}_{-0.13}$||$1.23^{+0.06}_{-0.06}$||$1.78^{+0.08}_{-0.08}$||$3.32^{+0.14}_{-0.15}$|
R200m〉 [h−1 Mpc]|$1.33^{+0.02}_{-0.02}$||$1.33^{+0.02}_{-0.02}$||$1.38^{+0.02}_{-0.02}$||$1.27^{+0.04}_{-0.04}$||$1.19^{+0.02}_{-0.02}$||$1.34^{+0.02}_{-0.02}$||$1.65^{+0.02}_{-0.03}$|
|$r_{\rm sp, model}^{\rm 3D}\ [h^{-1}\:\mbox{Mpc}]$||$1.61^{+0.01}_{-0.01}$||$1.61^{+0.01}_{-0.01}$||$1.63^{+0.01}_{-0.01}$||$1.45^{+0.03}_{-0.02}$||$1.41^{+0.01}_{-0.01}$||$1.62^{+0.01}_{-0.01}$||$1.92^{+0.01}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 3D} }{ {d}\ln r }|_{r=r_{\rm sp}^{\rm 3D}, {\rm model}}$||$-3.20^{+0.03}_{-0.03}$||$-3.11^{+0.03}_{-0.03}$||$-3.25^{+0.03}_{-0.03}$||$-3.27^{+0.05}_{-0.05}$||$-3.21^{+0.04}_{-0.04}$||$-3.28^{+0.03}_{-0.03}$||$-3.34^{+0.03}_{-0.03}$|
|$R_{\rm sp, model}^{\rm 2D}\ [h^{-1}\:\mbox{Mpc}]$||$1.12^{+0.01}_{-0.02}$||$1.07^{+0.03}_{-0.02}$||$1.16^{+0.01}_{-0.01}$||$1.07^{+0.04}_{-0.03}$||$0.99^{+0.01}_{-0.01}$||$1.13^{+0.01}_{-0.02}$||$1.39^{+0.02}_{-0.01}$|
|$\frac{ {d}\ln \xi _{\rm 2D} }{ {d}\ln R }|_{R=R_{\rm sp}^{\rm 2D}, {\rm model}}$||$-1.72^{+0.02}_{-0.02}$||$-1.67^{+0.01}_{-0.01}$||$-1.75^{+0.01}_{-0.01}$||$-1.75^{+0.03}_{-0.03}$||$-1.69^{+0.02}_{-0.02}$||$-1.75^{+0.02}_{-0.02}$||$-1.82^{+0.01}_{-0.01}$|

*We define each cluster sample by zcl, min, zcl, max, Nmin, and Nmax, and 〈zcl〉 and 〈N〉 give the mean values of cluster redshift and richness. We show the constraints for each sample on mean mass and R200m values, and the model predictions for the splashback feature from the mass–richness relation in Murata et al. (2019) and the halo emulator in Nishimichi et al. (2019) as described in appendix  1. We note that we use the halo–matter cross-correlation function for these model predictions and we compare our model predictions with model calculation methods in the literature in appendix  2. We show the median and the 16th and 84th percentiles of the model predictions using the fiducial results of the mass–richness relation in Murata et al. (2019) with the Planck cosmology.

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