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We thank Youngsoo Park for helping us to provide the data from his previous analysis and for the useful discussions on our mock tests. We thank Sebastian Bocquet and Matteo Costanzi for providing their cosmological chains to make Figure 14. This work was supported in part by World Premier International Research Center Initiative (WPI Initiative), MEXT, Japan, and JSPS KAKENHI Grant Numbers JP19K14767, 20H01932, JP20H05855, JP20H05861, JP21J10314, JP21K03625, 21H05456, JP22K21349, JP23H00108, and by Tokai Pathways to Global Excellence (T-GEx), part of MEXT Strategic Professional Development Program for Young Researchers. A part of numerical computations was carried out on Cray XC50 at the Center for Computational Astrophysics in NAOJ. RD acknowledges support from the NSF Graduate Research Fellowship Program under Grant No. DGE-2039656. XL is supported by the Department of Energy grant DE-SC0010118. INC
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We present cosmology results obtained from a blind joint analysis of the abundance, projected clustering, and weak lensing of galaxy clusters measured from the Sloan Digital Sky Survey (SDSS) redMaPPer cluster catalog and the Hyper-Suprime Cam (HSC) Year3 shape catalog. We present a full-forward model for the cluster observables, which includes empirical modeling for the anisotropic boosts on the lensing and clustering signals of optical clusters. We validate our analysis via mock cluster catalogs which include observational systematics, such as the projection effect and the effect of baryonic feedback, and find that our analysis can robustly constrain cosmological parameters in an unbiased manner without any informative priors on our model parameters. The joint analysis of our observables in the context of the flat ΛCDM model results in cosmological constraints for 𝑆8 ≡ 𝜎8 √︁ Ωm/0.3 = 0.816+0.041 −0.039. Our result is consistent with the 𝑆8 inference from other cosmic microwave background- and large scale structure-based cosmology analyses, including the result from the Planck 2018 primary CMB analysis.
Galaxy clusters are the most massive and gravitationally self-bound objects in the Universe, forming at rare high peaks in the initial density field (Bardeen et al. 1986; Kravtsov & Borgani 2012). Their abundance and time evolution are highly sensitive to the growth of structure in the Universe (Haiman et al. 2001; Lima & Hu 2005), making them an important tool for constraining cosmological parameters (Planck Collaboration et al. 2016; Bocquet et al. 2019; Abbott et al. 2020; Lesci et al. 2022, see also Weinberg et al. 2013 for a review). Many current and future galaxy surveys, including the Hyper Suprime-Cam survey (Aihara et al. 2018, HSC), the Dark En-ergy Survey1 (The Dark Energy Survey Collaboration 2005, DES), the Kilo Degree Survey2 (Kuijken et al. 2015, KiDS), the Rubin Observatory Legacy Survey of Space and Time3 (LSST Science Collaboration et al. 2009, LSST), Euclid4 (Amendola et al. 2018), and the Nancy Grace Roman Telescope5 (Dore et al. 2019), will provide unprecedented numbers of clusters and enable us to carry out optical cluster cosmology analyses with great precision if all the systematic effects are under control. In particular, photometric surveys allow for uniform and complete observations of clusters (Rykoff et al. 2014; Rozo & Rykoff 2014; Rozo et al. 2015a,b; Oguri 2014), which makes optically identified clusters from photometric surveys an interesting cosmological probe. These surveys also detect the weak lensing signal around clusters by observing the shapes of galaxies in their background. By combining the observed cluster abundances with the halo mass information measured from the cluster lensing signal (Johnston et al. 2007; von der Linden et al. 2014; Mantz et al. 2015; Simet et al. 2017; Murata et al. 2018; McClintock et al. 2019; Murata et al. 2019; Chiu et al. 2022), it is possible to carry out a self-contained analysis that both calibrates cluster masses and constrains cosmology (Lima & Hu 2005; Takada & Bridle 2007; Rozo et al. 2010; Oguri & Takada 2011; Chiu et al. 2023). Recent studies, such as Costanzi et al. (2019) and DES Collaboration et al. (2020) have calibrated cluster masses using cluster lensing signals, and used these calibrations to simultaneously constrain cosmology and the mass-observable relation (MOR) using cluster abundances. Both studies (especially the latter) found that the resulting cosmological constraints favored lower values of Ωm and higher values of 𝜎8 compared to other constraints derived from cosmic microwave background (CMB) or large-scale structure (LSS) data. These findings suggest the presence of yet unknown systematic effects for optical clusters. One of the main systematic effects for optically identified clusters is the so-called projection effect in which interloper galaxies along the line-of-sight (LOS) to a cluster are mistakenly identified as members of the cluster. Projection effects alter the mass-observable relation such that the observable for the optical clusters, which is the probability weighted sum of member galaxies (also called richness), is boosted with respect to its halo mass (Costanzi et al. 2019). In addition to the alteration of the mass-richness relation, Sunayama et al. (2020, SP20 henceforth) found that projection effects boost the amplitude of cluster lensing and clustering signals on large scales due to the preferential identification of clusters lying at the nodes of filaments aligned with the LOS direction. This results in an anisotropic distribution of matter around optical clusters, which breaks the isotropic halo model generally assumed to carry out lensing mass calibrations for clusters. Therefore, these anisotropic boosts inevitably lead to errors in the cluster mass calibrations, if not properly modeled These anisotropic boosts have been parameterized in a few cluster cosmology analyses. To et al. (2021) modeled the boost in their combined cosmology analysis with DES Y1 galaxies and redMaPPer clusters and obtained a similar size of the boost as the study by SP20. In previous work, Park et al. (2022, PS22 henceforth) also employed this boost model in a cluster cosmology analysis of the abundance, clustering and lensing signal of the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) cluster catalog (Rykoff et al. 2014), constructed from the Sloan Digital Sky Survey (SDSS) DR8 data (Aihara et al. 2011). While the value for the boost parameter inferred in PS22 was consistent with the inference in To et al. (2021), the resulting cosmological constraints favored low Ωm and high 𝜎8, similar to Costanzi et al. (2019) and DES Collaboration et al. (2020). This raises the question of how exactly the boost manifests in the observables of the real data, i.e., in the measured abundance, lensing and clustering signals. In particular, PS22 found hints of internal tensions among the different sectors of the data vector from a series of post-unblinding analyses. One of the findings from these tests is significant underprediction of the measured cluster lensing signals under a Planck cosmology (see Fig. 10 in PS22). This finding motivates our study using the cluster lensing signals measured from the HSC Year 3 (HSC-Y3) data, which has significantly deeper photometry and better image quality than the SDSS shape catalog and enables us to select source galaxies more securely and robustly due to reduced systematics related to intrinsic alignment and source-cluster member confusion. This paper is structured as follows. In Section 2, we describe the SDSS redMaPPer cluster catalog and the HSC-Y3 shape catalog as well as the HSC mock catalogs. In Section 3, we describe measurements of the cluster abundance, clustering, and lensing observables used in our analysis. In Section 4, we describe our analysis method and the theoretical model including emulator-based halo model predictions and models for cluster systematics. In Section 5, we discuss the validation tests against possible cluster systematics using mocks and validate our analysis through these tests. In Section 6, we present the result of our analysis pipeline on real data and a series of internal consistency tests. We finally summarize our study and discuss its implications in Section 7.
In this paper, we have presented a cluster cosmology analysis using the SDSS redMaPPer cluster and the HSC-Y3 shape catalog. We use an analysis that fully forward models the abundance, clustering, and lensing signals of galaxy clusters as developed in PS22. This previous study used cluster lensing signals measured from the SDSS data, and the result preferred low Ωm and high 𝜎8. However, in this study, we measure the weak lensing signals of the clusters using the HSCY3 shape catalog, which has significantly deeper photometry and better image quality than the SDSS source catalog, enabling source galaxy selections to be more secure and robust. With the lensing measurements from the HSC-Y3, the result of our cosmology analysis is consistent with the cosmology constraints from other CMB/LSS methods and surveys. Our work and findings can be summarized as follows. • Our cluster analysis constraints the 𝑆8 value, 𝑆8 = 0.816+0.041 −0.039. We note that our constraint on 𝑆8 agrees with the result from Planck 2018 within 1𝜎 and do not find any significant tensions with CMB or LSS measurements. • The difference between our analysis and PS22 comes from the weak lensing measurements of the clusters. Using the HSC-Y3 shape catalog enables us to securely select source galaxies. This makes our lensing measurement almost correction-free as shown in Fig. 1. • Due to a small area covered by the HSC-Y3 data, the lensing component of the covariance matrix is not shape-noise-dominated on large scales. Due to the loss of constraining power from the lensing signals on large scales (i.e., unable to resolve the degeneracy between the cluster bias and the anisotropic boost parameters), we decided to use the same anisotropic boost model for the clustering and lensing observables in all richness bins. • In addition to the validation tests done in PS22, we additionally consider baryonic effects on the lensing signals. For cluster-sized halos, baryonic effects suppress the amplitude of the lensing signals by ∼ 10% at scales 𝑅 ≤ 1ℎ −1Mpc. This suppression has negligible effects on the cosmology constraints. • Before unblinding, we carried out internal consistency tests with various analysis setups and found that the 𝑆8 parameter does not shift significantly in the different setups. In summary, our analysis performed well on the mocks and accurately constrained cosmological parameters against systematics including projection effects, mis-centering, photo-𝑧 scatter, and baryonic effects. Our cosmology results on real data are consistent with other CMB and LSS-based cosmology results, especially the CMB cosmology result from Planck 2018. In other words, we do not find any evidence of tension in the 𝑆8 values between our analysis and the Planck CMB analysis. However, we need to improve the precision of our measurements for this to be a conclusive statement. This is the first study that cosmological analysis using only optical cluster observables provides consistent results with other CMB/LSS cosmology analyses. In doing so, it was essential to model the anisotropic boosts due to projection effects and robustly measure the lensing signals with source galaxies well apart from lens clusters. The latter was possible due to superb image quality and depth of the HSC-Y3 data. However, our cluster cosmology analysis does not strongly constrain Ωm because our cluster analysis is done at a single richness bin. Evolution of cluster abundance is sensitive to Ωm (Bahcall 1995). An optically-selected cluster catalog from the HSC data (Oguri et al. 2018) (called CAMIRA cluster catalog) spans the redshift range of 0.1 ≤ 𝑧 ≤ 1.1, which enables us to track down the evolution of cluster abundance and is expected to give a tighter constrain on Ωm than our current work. Furthermore, we can self-calibrate the residual photo-𝑧 bias by using cluster samples at multiple redshift bins. Conducting cluster cosmology analysis on the HSC CAMIRA cluster catalog using our analysis method will be our future work.
Abbott T. M. C., et al., 2020, Phys. Rev. D, 102, 023509 Abbott T. M. C., et al., 2022, Phys. Rev. D, 105, 023520 Aihara H., et al., 2011, ApJS, 193, 29 Aihara H., et al., 2018, PASJ, 70, S4 Amendola L., et al., 2018, Living Reviews in Relativity, 21, 2 Bahcall N. A., 1995, in Mücket J. P., Gottloeber S., Müller V., eds, Large Scale Structure in the Universe. p. M (arXiv:astro-ph/9503111), doi:10.48550/arXiv.astro-ph/9503111 Bardeen J. M., Bond J. R., Kaiser N., Szalay A. S., 1986, ApJ, 304, 15 Baxter E. J., Rozo E., Jain B., Rykoff E., Wechsler R. H., 2016, MNRAS, 463, 205 Behroozi P. S., Wechsler R. H., Wu H.-Y., 2012, The Astrophysical Journal, 762, 109 Bocquet S., et al., 2019, ApJ, 878, 55 Bosch J., et al., 2017, Publications of the Astronomical Society of Japan, 70 Chiu I. N., et al., 2022, A&A, 661, A11 Chiu I. N., Klein M., Mohr J., Bocquet S., 2023, MNRAS, 522, 1601 Costanzi M., et al., 2019, MNRAS, 488, 4779 Costanzi M., et al., 2021, Phys. Rev. D, 103, 043522 DES Collaboration et al., 2020, Phys. Rev. D, 102, 023509 Dalal R., et al., 2023, arXiv e-prints, p. arXiv:2304.00701 Dore O., et al., 2019, BAAS, 51, 341 Farahi A., Evrard A. E., Rozo E., Rykoff E. S., Wechsler R. H., 2016, MNRAS, 460, 3900 Feroz F., Hobson M. P., 2008, MNRAS, 384, 449 Feroz F., Hobson M. P., Bridges M., 2009, MNRAS, 398, 1601 Feroz F., Hobson M. P., Cameron E., Pettitt A. N., 2019, The Open Journal of Astrophysics, 2, 10 Giri S. K., Schneider A., 2021, J. Cosmology Astropart. Phys., 2021, 046 Haiman Z., Mohr J. J., Holder G. P., 2001, ApJ, 553, 545 Hartlap J., Simon P., Schneider P., 2007, A&A, 464, 399 Hikage C., Mandelbaum R., Takada M., Spergel D. N., 2013, MNRAS, 435, 2345 Hikage C., et al., 2019, PASJ, 71, 43 Hinton S. R., 2016, The Journal of Open Source Software, 1, 00045 Hirata C., Seljak U., 2003, MNRAS, 343, 459 Hsieh B. C., Yee H. K. C., 2014, ApJ, 792, 102 Johnston D. E., et al., 2007, arXiv e-prints, p. arXiv:0709.1159 Kaiser N., 1992, ApJ, 388, 272 Kravtsov A. V., Borgani S., 2012, ARA&A, 50, 353 Kuijken K., et al., 2015, MNRAS, 454, 3500 LSST Science Collaboration et al., 2009, arXiv e-prints, p. arXiv:0912.0201 Landy S. D., Szalay A. S., 1993, ApJ, 412, 64 Lemos P., et al., 2023, MNRAS, 521, 1184 Lesci G. F., et al., 2022, A&A, 659, A88 Li X., et al., 2022, Publications of the Astronomical Society of Japan, 74, 421 Li X., et al., 2023, arXiv e-prints, p. arXiv:2304.00702 Lima M., Hu W., 2005, Phys. Rev. D, 72, 043006 Limber D. N., 1954, ApJ, 119, 655 Mandelbaum R., et al., 2018, Monthly Notices of the Royal Astronomical Society, 481, 3170 Mantz A. B., et al., 2015, MNRAS, 446, 2205 McClintock T., et al., 2019, MNRAS, 482, 1352 Miller L., et al., 2013, MNRAS, 429, 2858 Miyatake H., More S., Takada M., Spergel D. N., Mandelbaum R., Rykoff E. S., Rozo E., 2016, Phys. Rev. Lett., 116, 041301 Miyatake H., et al., 2021, arXiv e-prints, p. arXiv:2111.02419
Miyatake H., et al., 2023, arXiv e-prints, p. arXiv:2304.00704 More S., 2013, ApJ, 777, L26 More S., et al., 2023, arXiv e-prints, p. arXiv:2304.00703 Murata R., Nishimichi T., Takada M., Miyatake H., Shirasaki M., More S., Takahashi R., Osato K., 2018, ApJ, 854, 120 Murata R., et al., 2019, PASJ, 71, 107 Nakajima R., Mandelbaum R., Seljak U., Cohn J. D., Reyes R., Cool R., 2012, MNRAS, 420, 3240 Navarro J. F., Frenk C. S., White S. D. M., 1997, ApJ, 490, 493 Nishimichi T., et al., 2019, ApJ, 884, 29 Nishizawa A. J., Hsieh B.-C., Tanaka M., Takata T., 2020, Photometric Redshifts for the Hyper Suprime-Cam Subaru Strategic Program Data Release 2 (arXiv:2003.01511) Oguri M., 2014, MNRAS, 444, 147 Oguri M., Takada M., 2011, Phys. Rev. D, 83, 023008 Oguri M., et al., 2018, PASJ, 70, S20 Park Y., Sunayama T., Takada M., Kobayashi Y., Miyatake H., More S., Nishimichi T., Sugiyama S., 2022, Monthly Notices of the Royal Astronomical Society, 518, 5171 Planck Collaboration et al., 2016, A&A, 594, A24 Planck Collaboration et al., 2020, A&A, 641, A6 Rau M. M., et al., 2022, Weak Lensing Tomographic Redshift Distribution Inference for the Hyper Suprime-Cam Subaru Strategic Program threeyear shape catalogue (arXiv:2211.16516) Rozo E., Rykoff E. S., 2014, ApJ, 783, 80 Rozo E., et al., 2010, ApJ, 708, 645 Rozo E., Rykoff E. S., Bartlett J. G., Melin J.-B., 2015a, MNRAS, 450, 592 Rozo E., Rykoff E. S., Becker M., Reddick R. M., Wechsler R. H., 2015b, MNRAS, 453, 38 Rykoff E. S., et al., 2014, ApJ, 785, 104 Schneider A., Teyssier R., 2015, J. Cosmology Astropart. Phys., 2015, 049 Schneider A., Teyssier R., Stadel J., Chisari N. E., Le Brun A. M. C., Amara A., Refregier A., 2019, J. Cosmology Astropart. Phys., 2019, 020 Shirasaki M., Yoshida N., 2014, The Astrophysical Journal, 786, 43 Shirasaki M., Takada M., Miyatake H., Takahashi R., Hamana T., Nishimichi T., Murata R., 2017, Monthly Notices of the Royal Astronomical Society, 470, 3476 Shirasaki M., Hamana T., Takada M., Takahashi R., Miyatake H., 2019, Monthly Notices of the Royal Astronomical Society, 486, 52 Shirasaki M., Miyatake H., Hilton M., et al., in prep., Masses of SunyaevZel’dovich Galaxy Clusters Detected by The Atacama Cosmology Telescope: Stacked Lensing Measurements with Subaru HSC Year 3 data Simet M., McClintock T., Mandelbaum R., Rozo E., Rykoff E., Sheldon E., Wechsler R. H., 2017, MNRAS, 466, 3103 Sugiyama S., et al., 2023, arXiv e-prints, p. arXiv:2304.00705 Sunayama T., 2023, MNRAS, 521, 5064 Sunayama T., More S., 2019, MNRAS, 490, 4945 Sunayama T., et al., 2020, MNRAS, 496, 4468 Takada M., Bridle S., 2007, New Journal of Physics, 9, 446 Takahashi R., Hamana T., Shirasaki M., Namikawa T., Nishimichi T., Osato K., Shiroyama K., 2017, The Astrophysical Journal, 850, 24 Tanaka M., et al., 2017, Publications of the Astronomical Society of Japan, 70 The Dark Energy Survey Collaboration 2005, arXiv e-prints, pp astro– ph/0510346 To C., et al., 2021, Phys. Rev. Lett., 126, 141301 Weinberg D. H., Mortonson M. J., Eisenstein D. J., Hirata C., Riess A. G., Rozo E., 2013, Phys. Rep., 530, 87 Zhang Y., et al., 2019, MNRAS, 487, 2578 Zuntz J., et al., 2015, Astronomy and Computing, 12, 45 van den Bosch F. C., More S., Cacciato M., Mo H., Yang X., 2013, Monthly Notices of the Royal Astronomical Society, 430, 725–746 von der Linden A., et al., 2014, MNRAS, 439, 2
