X-ray properties of high-richness CAMIRA clusters in the Hyper Suprime-Cam Subaru Strategic Program field

X-ray properties of high-richness CAMIRA clusters in the Hyper Suprime-Cam Subaru Strategic Program field


We present the first results of a pilot X-ray study of 17 rich galaxy clusters at in the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) field. Diffuse X-ray emissions from these clusters were serendipitously detected in the XMM-Newton fields of view. We systematically analyze X-ray images and emission spectra of the hot intracluster gas by using the XMM-Newton archive data. The frequency distribution of the offset between the X-ray centroid or peak and the position of the brightest cluster galaxy was derived for the optically-selected cluster sample. The fraction of relaxed clusters estimated from the X-ray peak offsets is %, which is smaller than that of the X-ray cluster samples such as HIFLUGCS. Since the optical cluster search is immune to the physical state of X-ray-emitting gas, it is likely to cover a larger range of the cluster morphology. We also derived the luminosity-temperature relation and found that the slope is marginally shallower than those of X-ray-selected samples and consistent with the self-similar model prediction of 2. Accordingly, our results show that the X-ray properties of the optically-selected clusters are marginally different from those observed in the X-ray samples. The implication of the results and future prospects are briefly discussed.


reception date \Acceptedacception date \Publishedpublication date


cosmology: observations — galaxies: clusters: intergalactic medium — X-rays: galaxies: clusters

1 Introduction

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP; Aihara et al., 2018a, b; Tanaka et al., 2018; Bosch et al., 2018) is an ongoing wide-field imaging survey that uses the HSC (Miyazaki et al., 2012, 2015, 2018; Komiyama et al., 2018; Kawanomoto et al., 2018; Furusawa et al., 2018) mounted on the prime focus of the Subaru Telescope. The HSC-SSP survey has three different layers, Wide, Deep, and Ultra-deep. The wide layer takes five-band () and deep ( AB mag) imaging over  deg. To date, the survey covers  deg with non-full-depth and  deg with the full-depth and full-color (Aihara et al., 2018a).

The deep and multi-band HSC-SSP imaging gives us a unique opportunity to conduct a systematic search of optical galaxy clusters. In fact, Oguri et al. (2018) discovered 2000 galaxy clusters with richness in  deg, by applying the CAMIRA algorithm developed by Oguri (2014). The galaxy clusters are discovered as concentrations of red-sequence galaxies by applying a compensated spatial filter to the three-dimensional richness map. The accuracy of photometric redshifts of the CAMIRA clusters is .

The CAMIRA catalog features a wide redshift coverage and a low mass limit, which therefore provides us with an unprecedented cluster sample including high-redshift objects. Because the limiting magnitudes of the HSC-SSP survey is much deeper than those of the Sloan Digital Sky Survey (SDSS) and Dark Energy Survey (DES), the galaxy clusters can be securely identified up to , in contrast with the SDSS (; Oguri, 2014; Rykoff et al., 2014) and the DES (; Rykoff et al., 2016). The redshift range is comparable to those covered by Sunyaev-Zel’dovich effect (SZE) surveys, which used the South Pole Telescope (Bleem et al., 2015) and the Atacama Cosmology Telescope (Hilton et al., 2017). The richness roughly corresponds to (Oguri et al., 2018) and is equivalent to if we assume a median halo concentration of (Diemer & Kravtsov, 2015). The detection limit of the cluster mass for the CAMIRA clusters is then much lower than those of the SZE clusters (; Bleem et al., 2015).

To understand the gas physics and establish scaling relations between cluster mass and X-ray observables in preparation for future cosmological research, it is important to systematically study the X-ray properties of the optically-selected clusters and compare them with other multi-wavelength surveys. To date, a number of systematic cluster observations (see, e.g., Vikhlinin et al., 2006; Zhang et al., 2008; Sun et al., 2009; Martino et al., 2014; Mahdavi et al., 2013; Donahue et al., 2014; von der Linden et al., 2014; Okabe et al., 2014; Hoekstra et al., 2015; Smith et al., 2016; Mantz et al., 2016) have been conducted by referring to cluster catalogs constructed from the ROSAT All Sky Survey (RASS; e.g., Böhringer et al., 2001). More recently, statistical studies use the cutting-edge X-ray surveys (e.g., Pierre et al., 2016a), SZE (e.g., Sanders et al., 2017), or optical techniques (e.g., Hicks et al., 2008, 2013; Takey et al., 2013). Since different survey techniques have their own selection functions, some systematic differences may appear in their observed cluster properties and scaling relations. If this happens, a selection bias issue arises, which eventually leads to a difficulty in constraining the cosmological models using the cluster mass function (see, e.g., Allen et al., 2011; Giodini et al., 2013). This will have an impact on interpretation of the upcoming eROSITA (Merloni et al., 2012) and other ongoing/future large-scale cluster surveys.

A useful measure of the cluster dynamical state is given by the offset between the location of the brightest cluster galaxy (BCG) and the X-ray centroid or X-ray peak (e.g., Katayama et al., 2003). The X-ray centroid (or peak) offset is sometimes used to classify the clusters into relaxed and disturbed clusters (Mann & Ebeling, 2012; Mahdavi et al., 2013; Rossetti et al., 2016). Rossetti et al. (2016) showed that the fraction of relaxed clusters is smaller in the Planck sample than that in the X-ray samples, indicating that SZE and X-rays surveys of galaxy clusters are affected by the different selection effects. In this way, the X-ray centroid shift is useful not only to characterize the cluster dynamical state but also to study the selection effect. While the offsets between optical and X-ray centers have been used to study the misidentification of central galaxies in optical cluster finding algorithms (e.g., Rozo & Rykoff, 2014; Rykoff et al., 2016; Oguri et al., 2018), dynamical states of optically selected clusters based on offset distributions have not yet been fully explored. To address this situation, this paper presents a systematic measurement of the centroid shift in the optical sample.

We thus carried out a systematic X-ray analysis of the CAMIRA clusters with high optical richness using the XMM-Newton archival data. Section 2 presents the sample selection and section 3 describes the data analyses regarding centroid determination and spectral analysis. Section 4 derives the centroid shift and the luminosity-temperature relation, and section 5 discusses the implication of the results. Finally section 6 summarizes the results and briefly discusses the future prospects of this X-ray follow-up project.

The cosmological parameters are , and throughout this paper, and we use the proto-solar abundance table from Lodders & Palme (2009). Unless otherwise noted, the quoted errors represent the statistical uncertainties.

2 Sample

The CAMIRA catalog comprises 2086 clusters at in the S16A Wide and Deep fields (Oguri et al., 2018), whose redshift distributions are shown in Figure 1. We cross-correlated the CAMIRA catalog with the 3XMM-DR7 catalog (Rosen et al., 2016) to find that there are X-ray sources within from the optical centers. We then excluded a XXL survey region overlapped with that of the HSC-SSP survey from the above search result; an X-ray study in the XXL field is to be done through the HSC-XXL external collaboration. To do the systematic X-ray analysis of high-richness clusters, we constructed the sample by selecting objects with the richness and good-quality XMM-Newton archival data. For the latter, we require typically more than 1000 cluster-photon counts so as to enable X-ray spectroscopic measurements of the gas temperature and luminosity. Therefore, as listed in Table 2, the present sample consists of 17 clusters at , whose distribution is overlaid on that of the entire CAMIRA catalog (Figure 1). Except for HSC J141508-002936 at (alternative name is Abell 1882), X-ray emissions from these clusters are serendipitously detected inside the XMM-Newton fields of view. The average (median) redshift is 0.40 (0.33). Examples of HSC images of the CAMIRA clusters are shown in Figure 2.

Table 2 lists the location of BCGs identified by the CAMIRA algorithm (Oguri et al., 2018). Note that for 3 out of 17 clusters, the BCGs are clearly misidentified by the CAMIRA algorithm. Since we are interested in physical offsets between BCGs and X-ray peaks rather than miscentering of optical cluster finding algorithms, we correct the BCG coordinates for these three (HSC J140309-001833, HSC J021427-062720, HSC J100049+013820) by visual inspection of their HSC images.


Sample list. Cluster BCG position X-ray centroid OBSID Exposure (Mpc/\arcsec) RA, Dec (deg) RA, Dec (deg) (kpc) (kpc) M1, M2, PN HSC J142624-012657 0.460 69.7 0.835 / 142 216.6011, -1.4492 216.6005, -1.4491 12 23 0674480701 12.3, 12.6, 9.1 HSC J021115-034319 0.745 52.3 0.653 / 88 32.8135 , -3.7219 32.8142 , -3.7232 38 60 0655343861 7.7 , 13.0, 3.4 HSC J095939+023044 0.730 51.7 0.657 / 90 149.9132, 2.5122 149.9185, 2.5201 250 196 0203361701 30.1, 30.2, 24.3 HSC J161136+541635 0.332 48.3 0.807 / 168 242.8998, 54.2763 242.8967, 54.2775 37 38 0059752301 4.9 , 4.7 , 2.9 HSC J090914-001220 0.303 46.5 0.811 / 180 137.3075, -0.2056 137.3089, -0.2057 24 23 0725310142 2.5 , 2.7 , 2.4 HSC J141508-002936 0.144 43.0 0.860 / 340 213.7850, -0.4932 213.7729, -0.4884 119 50 0145480101 11.0, 11.8, 6.9 HSC J140309-001833 0.449 39.7 0.715 / 124 210.7876, -0.3091 210.7939, -0.3069 76 36 0606430501 20.4, 21.1, 13.3 HSC J095737+023426 0.372 37.4 0.734 / 142 149.4043, 2.5738 149.4052, 2.5750 27 14 0203362201 28.9, 29.0, 12.3 HSC J022135-062618 0.300 35.7 0.754 / 169 35.3947 , -6.4384 35.4069 , -6.4457 228 10 0655343837 2.6 , 2.6 , 2.2 HSC J232924-004855 0.310 35.2 0.746 / 163 352.3487, -0.8154 352.3495, -0.8147 17 44 0673002346 3.5 , 3.8 , 1.8 HSC J022512-062259 0.202 33.0 0.775 / 232 36.3012 , -6.3831 36.2933 , -6.3865 102 246 0655343836 2.6 , 2.5 , 2.2 HSC J021427-062720 0.246 31.3 0.746 / 192 33.6071 , -6.4607 33.6173 , -6.4566 151 15 0655343859 2.5 , 2.7 , 2.0 HSC J161039+540554 0.330 29.5 0.702 / 147 242.6626, 54.0983 242.6706, 54.1014 97 144 0059752301 4.9 , 4.8 , 2.9 HSC J095903+025545 0.332 26.4 0.679 / 142 149.7614, 2.9291 149.7611, 2.9219 123 8 0203361601 19.1, 0.0 , 8.6 HSC J100049+013820 0.228 23.2 0.692 / 189 150.1898, 1.6573 150.1986, 1.6574 116 94 0302351001 37.6, 38.9, 28.0 HSC J090743+013330 0.172 23.1 0.711 / 242 136.9295, 1.5583 136.9445, 1.5577 159 14 0725310156 2.6 , 2.7 , 2.4 HSC J095824+024916 0.341 20.1 0.625 / 128 149.6001, 2.8212 149.5998, 2.8215 8 9 0203362101 59.4, 59.5, 51.1 {tabnote} Richness. Centroid shift (see section 3.2 for definition). Peak shift (see section 3.2 for definition). The XMM-Newton observation id. The XMM-Newton EPIC-MOS1(M1), MOS2(M2), and PN exposure time after data filtering (ksec).

Figure 1: Redshift distributions of the present sample (black), CAMIRA HSC S16A Wide (blue) and Deep (magenta) cluster catalogs (Oguri et al., 2018). The binsize is and each histogram is normalized such that the integral over the range is unity. The vertical dashed line indicates the median redshift of the present sample, .
Figure 2: Examples of HSC member galaxy density maps smoothed with FWHM kpc (upper panels) and I-band images (lower panels) of the CAMIRA clusters, HSC J161136+541635 at (left panels) and HSC J161039+540554 at (right panels). In each panel, the X-ray centroid and BCG positions are marked with a magenta “” and white “+”, respectively. The white contours are linearly spaced by half of the average height of galaxy density maps over all CAMIRA clusters at the same redshift. The red contours for X-ray emission are ten levels logarithmically spaced from .

3 Analysis

3.1 Data reduction

Observation data files were retrieved from the XMM-Newton Science Archive17 and reprocessed with the XMM-Newton Science Analysis System v15.0.0 and the Current Calibration Files. The data reduction, including flare screening, point source detection, and estimation of the quiescent particle background, was done in the standard manner by using the XMM Extended Source Analysis Software [ESAS; Snowden et al. (2008); see also Miyaoka et al. (2018)].

3.2 Centroid determination

The X-ray centroid of each cluster was determined from the mean of the photon distribution in an aperture circle of radius . This analysis used the 0.4–2.3 keV EPIC composite image (one image pixel is 5\arcsec). Here, was calculated by substituting in Table 2 in the relation, which was deduced from the relation (Arnaud et al., 2005) and the relation (Oguri et al., 2018). Starting with the optical center, we iterated the centroid search until its position converged within 5\arcsec. If contaminating point sources remained in the circle, we excluded the region centered at the sources and the region symmetric to them so as not to affect the above calculation. The result is listed in Table 2. The offset between X-ray centroid and BCG position is presented in section 4.1.

3.3 Spectral analysis

To evaluate the gas temperature and bolometric luminosity, we derive the X-ray spectra by extracting the EPIC data from a circular region within a radius of centered on the X-ray centroid. The spectra were rebinned so that each spectral bin contains over 25 counts. After subtracting the quiescent particle background, the observed spectra of the EPIC MOS/PN cameras in the 0.3–10/0.4–10 keV band were simultaneously fit by using XSPEC 12.9.1 (Arnaud, 1996).

The spectral model consists of (i) cluster thermal emission and (ii) background components. For (i), we used the APEC thin-thermal plasma model with AtomDB version 3.0.8 (Smith et al., 2001; Foster et al., 2012) . The cluster redshift and metal abundance were fixed at the optical value [Table 2; Oguri et al. (2018)] and at 0.3 solar, respectively. The Galactic hydrogen column density was fixed at a value taken from the Leiden/Argentine/Bonn survey (Kalberla et al., 2005). For (ii), the Galactic emission and the cosmic X-ray background were evaluated by jointly fitting the RASS spectra (Snowden et al., 1997) taken from the ring region around the cluster. The other components due to possible solar wind charge exchange, soft proton events, and instrumental fluorescent lines were added to the model. An example of the spectral fitting is shown in Figure 3. The resultant APEC model parameters are summarized in Table 3. The bolometric luminosity was estimated from the best-fit model flux in the source-frame energy range of 0.01 – 30 keV.

The XMM + RASS joint fitting gives a reasonable result for most of clusters; however, the background subtraction is not perfect at high energies, particularly for the three clusters, HSC J021115-034319, HSC J021427-062720, and HSC J161039+540554. This is likely to be due to the residual soft proton flares, as indicated by the count-rate ratio between in-FOV and out-FOV (De Luca & Molendi, 2004). Thus, to check the background uncertainty, we subtract the local background extracted from an annulus centered on the X-ray centroid and fit the APEC model to the observed spectra. Since the resultant parameters are consistent with those obtained from the XMM + RASS joint analysis within that statistics for 14 clusters, we quote the values obtained from the analysis by using the local background for the three clusters mentioned above (see Table 3).

Figure 3: Example of spectral fit. The upper panel shows the MOS1 (black), MOS2 (red), and PN (green) spectra of HSC J161136+541635 at and the RASS background spectrum (blue). The solid and dotted lines represent the total model consisting of cluster emission and backgrounds (see section 3.3) and the residual soft-proton background component, respectively. The lower panel shows the residual of the fit.

Results of spectral analysis under APEC thermal plasma model. Cluster /d.o.f. () (keV) () HSC J142624-012657 3.21 60.4 / 60 HSC J021115-034319 1.95 92.1 / 81 HSC J095939+023044 1.71 113.0 / 107 HSC J161136+541635 0.95 73.5 / 69 HSC J090914-001220 2.73 48.6 / 33 HSC J141508-002936 3.27 249.0 / 212 HSC J140309-001833 3.66 115.7 / 122 HSC J095737+023426 1.85 149.6 / 154 HSC J022135-062618 2.73 25.1 / 13 HSC J232924-004855 4.33 56.4 / 32 HSC J022512-062259 2.95 76.1 / 71 HSC J021427-062720 2.13 58.0 / 61 HSC J161039+540554 0.94 51.4 / 43 HSC J095903+025545 1.79 108.6 / 112 HSC J100049+013820 1.80 55.7 / 51 HSC J090743+013330 3.20 23.8 / 23 HSC J095824+024916 1.84 328.8 / 283 {tabnote} The bolometric luminosity within the scale radius

4 Results

4.1 Centroid shift and peak shift

We define the centroid shift as a projected distance between the BCG coordinates and the X-ray centroid measured within . The measured centroid shift is given in Table 2. The histograms of the centroid shift in kpc and fractions of are shown in the upper panels of Figure 4. The median of the centroid shift is  kpc or .

Next, we measured the X-ray peak position within by using the XMM composite image smoothed with a  (pixels) Gaussian function. We define the peak shift as a projected distance relative to the BCG coordinates. The resultant peak shift is shown in Table 2. As discussed in Mann & Ebeling (2012), the accuracy of the X-ray peak position depends on the statistical quality of the X-ray observations as well as the surface brightness distribution, which varies significantly between clusters. We assessed the standard error of the peak shift by comparing X-ray images of each cluster with different smoothing scale ( pixles). For 17 clusters, ranges from 4% to 160% (the mean is 25%). The lower panels of Figure 4 show the histograms of the measured peak shift in units of kpc and . The median is  kpc or .

We divide the sample into two classes, “relaxed” clusters with a small peak shift () and “disturbed” clusters with a large shift () following the criteria used in Sanderson et al. (2009). As a result, there are 5 (11) relaxed (disturbed) clusters and the fraction of relaxed objects is %. Here the error indicates the systematic uncertainty in the measurement and was estimated by comparing the X-ray images with different smoothing scale. Section 5.1 compares the fraction of relaxed clusters for the present optical sample with nearby X-ray and SZE cluster samples.

Figure 4: Histograms of centroid shift (upper panels) and peak shift (lower panels) in units of kpc and . In each panel, the vertical dashed line indicates the distribution median.

4.2 Luminosity-temperature relation

In the self-similar model, the redshift evolution of the cluster scaling relations is described by the factor and the luminosity of the cluster gas in the hydrostatic state follows . Within this framework, the normalization of the luminosity-temperature relation evolves as (Giles et al., 2016). Despite a number of observational studies, however, no clear consensus has been reached on the evolution of the scaling relations (for a review, see Giodini et al., 2013). In the present paper, we correct the redshift evolution by applying the self-similar model and plot against gas temperature in the left panel of Figure 5.

We fit the observed relation to the power-law model (equation 1). To account for measurement errors in both variables, we use the Bayesian regression method (Kelly, 2007) because it has been demonstrated that it outperforms other common estimators that can constrain the parameters even when the measurement errors are large. The quantities , , and the intrinsic scatter are treated as free parameters.


The best-fit parameters are , , and .

For comparison, if we apply the BCES code (Akritas & Bershady, 1996) to the present optical sample, the fitting yields the best-fit slope steeper than 2.0 but with a fairly large uncertainty; namely, . Kelly (2007) noted that the BCES estimate of the slope tends to suffer some bias and becomes considerably unstable when the measurement errors are large and/or the sample size is small. Therefore, in section 5.2 we quote the above results based on the Bayesian regression method.

Figure 5: (left) Luminosity-temperature relation of the high-richness clusters. (right) Gas temperature - richness relation. In each panel, the dashed line shows the best-fit power-law model. In the right panel, the dotted line shows the best-fit relation derived for the XXL and XXL-LSS sample (Oguri et al., 2018).

5 Discussion

5.1 Centroid shift and the cluster dynamical state

In section 4.1 we quantified the centroid shift and peak shift from the XMM image analysis to find that half of the sample has the centroid (peak) shift larger than () or 85 kpc (36 kpc). Following the criteria used in Sanderson et al. (2009), Rossetti et al. (2016) estimated the fraction of relaxed clusters in the Planck SZE sample to be %. They also calculated the fraction to be % in X-ray selected cluster samples constructed from the HIFLUGCS, MACS, and REXCESS surveys, whereas we obtain only % from our optical sample. This suggests that the optically-selected sample contains a larger fraction of merging clusters with disturbed morphology particularly in comparison with the X-ray selected cluster samples.

X-ray observations preferentially detect relaxed clusters having cool cores at the center as opposed to more disturbed, non-cool-core clusters found in SZE surveys (Eckert et al., 2011; Rossetti et al., 2017; Andrade-Santos et al., 2017). Furthermore, Chon & Böhringer (2017) claim that the cool-core bias in previous X-ray surveys is due to the survey-selection method such as for a flux-limited survey, and is not due to the inherent nature of X-ray selection. Therefore, considering the nature of the HSC cluster survey, we suggest that the observed small fraction of relaxed clusters in the present optical sample is due to the fact that the CAMIRA algorithm is immune to the dynamical state of X-ray-emitting gas and is likely to detect clusters with a wider range of cluster morphology.

Given a higher merger rate in the distant universe, the redshift evolution of X-ray morphology is likely to affect the measurement of the fraction of relaxed clusters with respect to disturbed clusters. Mann & Ebeling (2012) reported based on the Chandra observations that the fraction of merging clusters increases at for the X-ray luminous clusters. The redshift evolution is, however, only marginally seen in our optically-selected cluster sample; the fractions of relaxed clusters estimated from the X-ray peak shifts are % at and % at .

5.2 Scaling relations

The slope of of the relation derived for the present optically-selected clusters (section 4.2) is consistent with the slope of 2.0 predicted from the self-similar model, whereas a steeper slope of has been reported by many X-ray observations in the past (for review, see Giodini et al., 2013). Even so, the data points lie within the observed large scatter of X-ray clusters on the plane (Takey et al., 2011).

The fitted slope agrees with that of the Red-sequence Cluster Survey at high redshifts [the slope parameter is ; Hicks et al. (2008)] and that of the total RCS sample; namely, 18 clusters at [; Hicks et al. (2013)] within the errors. In comparison with X-ray selected samples that contain a large number of clusters () at a wide redshift range [; Reichert et al. (2011), ; Takey et al. (2011), ; Maughan et al. (2012)], the present sample shows a marginally shallower slope. To further confirm the result, however, we need to increase the number of clusters and improve the accuracy with which the relation is measured.

The right panel of Figure 5 shows the relationship between gas temperature and optical richness. Although the scatter is large, the positive correlation is seen and the correlation coefficient is calculated to be 0.63. Assuming the power-law model,


the fit to the data yields and . This is marginally steeper than the best-fit power-law relation derived for 50 bright X-ray clusters in the XXL and XXL-LSS fields [, ; Oguri et al. (2018)]. Because the gas temperature of XXL and XXL-LSS clusters was measured in the central  kpc region (Pierre et al., 2004, 2016b), direct comparison is not easy. Conversely, the self-similar model predicts given that the cluster mass is related to richness and temperature through and , respectively. Thus our fitting result is consistent with the self-similar model, although the statistical uncertainty is large.

6 Summary and future prospects

Using the XMM-Newton archive data, we apply an X-ray analysis to 17 rich, optically-selected clusters of galaxies at in the HSC-SSP field. Most of the clusters were serendipitously detected in the XMM-Newton fields of view. The major findings are as follows:

  1. We systematically analyzed the X-ray centroid or peak shift as compared with the BCG position. The fraction of relaxed clusters in the optically-selected cluster sample, which is defined based on the offset between the BCG and X-ray peak, is %. This is less than that of the X-ray samples. Because the optical sample is immune to the cool-core bias, it is likely to contain more irregular clusters and thus cover a larger range of the cluster morphology.

  2. The slope of the luminosity-temperature relation is marginally less than that of X-ray samples and is consistent with the self-similar model prediction of 2.0. The slope of the temperature-richness relation is also consistent with the prediction of the self-similar model although the former has a large statistical uncertainty.

Our results provide important information about the X-ray properties of the optically-selected clusters, which are marginally different from those observed in the X-ray samples. To obtain more conclusive results, we need to improve the measurement accuracy. We thus plan to extend the analysis by (1) incorporating fainter objects in the 3MM-DR7 catalog and (2) conducting X-ray observations of the massive, high-redshift () clusters newly discovered by the HSC-SSP survey. For the latter, the XMM-Newton follow-up project is now ongoing and is to be the subject of an upcoming presentation. Furthermore, by the time of completion of the HSC-SSP survey, the CAMIRA cluster catalog will be about 6-times larger than that at present. These works should allow us to derive the mass-observable scaling by using a larger number of clusters and study the redshift evolution of the X-ray properties of the optical clusters. Detailed comparisons of optical, weak lensing, SZE, and X-ray selected clusters will improve our knowledge of cluster-mass calibration and cluster evolution.


The Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan, and Princeton University. The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University.

This paper makes use of software developed for the Large Synoptic Survey Telescope. We thank the LSST Project for making their code available as free software at http://dm.lsst.org

The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under Grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE) and the Los Alamos National Laboratory.

We are grateful to Chien-Hsiu Lee for useful comments. This work was supported in part by JSPS KAKENHI grants 16K05295 (NO) and JP15K17610 (SU). YI is financially supported by a Grant-in-Aid for JSPS Fellows (16J02333).


  1. affiliation: Department of Physics, Nara Women’s University, Kitauoyanishi-machi, Nara, Nara 630-8506, Japan
  2. affiliation: Department of Physics, Nagoya University, Aichi 464-8602, Japan
  3. affiliation: Department of Physics, Nagoya University, Aichi 464-8602, Japan
  4. affiliation: SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
  5. affiliation: Department of Physics, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
  6. affiliation: Institute of Space and Astronautical Science (ISAS), JAXA, 3-1-1 Yoshinodai, Chuo, Sagamihara, Kanagawa 252-5210, Japan
  7. affiliation: Department of Physical Science, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan
  8. affiliation: Hiroshima Astrophysical Science Center, Hiroshima University, 1-3-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan
  9. affiliation: Core Research for Energetic Universe, Hiroshima University, 1-3-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan
  10. affiliation: Research Center for the Early Universe, University of Tokyo, Tokyo 113-0033, Japan
  11. affiliation: Department of Physics, University of Tokyo, Tokyo 113-0033, Japan
  12. affiliation: Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), University of Tokyo, Chiba 277-8582, Japan
  13. affiliation: National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588, Japan
  14. affiliation: Department of Physical Science, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan
  15. affiliation: National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588, Japan
  16. affiliation: Department of Astronomy, School of Science, Graduate University for Advanced Studies, Mitaka, Tokyo 181-8588, Japan
  17. http://nxsa.esac.esa.int


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