arXiv subjects

Masato Shirasaki

Publications and source records attributed to Masato Shirasaki.

At least 55 records · Page 3Linked to original sources

arXiv2021

HSC Year 1 cosmology results with the minimal bias method: HSC$\times$BOSS galaxy-galaxy weak lensing and BOSS galaxy clustering

We present cosmological parameter constraints from a blinded joint analysis of galaxy-galaxy weak lensing, $Δ\!Σ(R)$, and projected correlation function, $w_\mathrm{p}(R)$, measured from the first-year HSC (HSC-Y1) data and SDSS spectroscopic galaxies over $0.15 0.75$ for the $Δ\!Σ$ measurements, selected based on their photometric redshifts. For theoretical template, we use the "minimal bias" model for the cosmological clustering observables for the flat $Λ$CDM cosmological model. We compare the model predictions with the measurements in each redshift bin on large scales, $R>12$ and $8~h^{-1}\mathrm{Mpc}$ for $Δ\!Σ(R)$ and $w_\mathrm{p}(R)$, respectively, where the perturbation theory-inspired model is valid. When we employ weak priors on cosmological parameters, without CMB information, we find $S_8=0.936^{+0.092}_{-0.086}$, $σ_8=0.85^{+0.16}_{-0.11}$, and $Ω_\mathrm{m}=0.283^{+0.12}_{-0.035}$ for the flat $Λ$CDM model. Although the central value of $S_8$ appears to be larger than those inferred from other cosmological experiments, we find that the difference is consistent with expected differences due to sample variance, and our results are consistent with the other results to within the statistical uncertainties. (abriged)

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arXiv2021

Noise reduction for weak lensing mass mapping: An application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data

We propose a deep-learning approach based on generative adversarial networks (GANs) to reduce noise in weak lensing mass maps under realistic conditions. We apply image-to-image translation using conditional GANs to the mass map obtained from the first-year data of Subaru Hyper Suprime-Cam (HSC) survey. We train the conditional GANs by using 25000 mock HSC catalogues that directly incorporate a variety of observational effects. We study the non-Gaussian information in denoised maps using one-point probability distribution functions (PDFs) and also perform matching analysis for positive peaks and massive clusters. An ensemble learning technique with our GANs is successfully applied to reproduce the PDFs of the lensing convergence. About $60\%$ of the peaks in the denoised maps with height greater than $5σ$ have counterparts of massive clusters within a separation of 6 arcmin. We show that PDFs in the denoised maps are not compromised by details of multiplicative biases and photometric redshift distributions, nor by shape measurement errors, and that the PDFs show stronger cosmological dependence compared to the noisy counterpart. We apply our denoising method to a part of the first-year HSC data to show that the observed mass distribution is statistically consistent with the prediction from the standard $Λ$CDM model.

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arXiv2021

Virial halo mass function in the ${\it Planck}$ cosmology

We study halo mass functions with high-resolution $N$-body simulations under a $Λ$CDM cosmology. Our simulations adopt the cosmological model that is consistent with recent measurements of the cosmic microwave backgrounds with the ${\it Planck}$ satellite. We calibrate the halo mass functions for $10^{8.5} \lower.5ex\hbox{$\; \buildrel < \over \sim \;$} M_\mathrm{vir} / (h^{-1}M_\odot) \lower.5ex\hbox{$\; \buildrel < \over \sim \;$} 10^{15.0 - 0.45 \, z}$, where $M_\mathrm{vir}$ is the virial spherical overdensity mass and redshift $z$ ranges from $0$ to $7$. The halo mass function in our simulations can be fitted by a four-parameter model over a wide range of halo masses and redshifts, while we require some redshift evolution of the fitting parameters. Our new fitting formula of the mass function has a 5\%-level precision except for the highest masses at $z\le 7$. Our model predicts that the analytic prediction in Sheth $\&$ Tormen would overestimate the halo abundance at $z=6$ with $M_\mathrm{vir} = 10^{8.5-10}\, h^{-1}M_\odot$ by $20-30\%$. Our calibrated halo mass function provides a baseline model to constrain warm dark matter (WDM) by high-$z$ galaxy number counts. We compare a cumulative luminosity function of galaxies at $z=6$ with the total halo abundance based on our model and a recently proposed WDM correction. We find that WDM with its mass lighter than $2.71\, \mathrm{keV}$ is incompatible with the observed galaxy number density at a $2σ$ confidence level.

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arXiv2021

Stacked phase-space density of galaxies around massive clusters: Comparison of dynamical and lensing masses

We present a measurement of average histograms of line-of-sight velocities over pairs of galaxies and galaxy clusters. Since the histogram can be measured at different galaxy-cluster separations, this observable is commonly referred to as the stacked phase-space density. We formulate the stacked phase-space density based on a halo-model approach so that the model can be applied to real samples of galaxies and clusters. We examine our model by using an actual sample of massive clusters with known weak-lensing masses and spectroscopic observations of galaxies around the clusters. A likelihood analysis with our model enables us to infer the spherical-symmetric velocity dispersion of observed galaxies in massive clusters. We find the velocity dispersion of galaxies surrounding clusters with their lensing masses of $1.1\times10^{15}\, h^{-1}M_{\odot}$ to be $1180^{+83}_{-70}\, \mathrm{km/s}$ at the 68\% confidence level. Our constraint confirms that the relation between the galaxy velocity dispersion and the host cluster mass in our sample is consistent with the prediction in dark-matter-only N-body simulations under General Relativity. Assuming that the Poisson equation in clusters can be altered by an effective gravitational constant of $G_\mathrm{eff}$, our measurement of the velocity dispersion can place a tight constraint of $0.88 < G_\mathrm{eff}/G_\mathrm{N} < 1.29\, (68\%)$ at length scales of a few Mpc about $2.5$ Giga years ago, where $G_\mathrm{N}$ is the Newton's constant.

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arXiv2021

Searching for eV-mass Axion-like Particles with Cross Correlations between Line Intensity and Weak Lensing Maps

We study cross correlations between line intensity and weak lensing maps to search for axion-like particles (ALPs). Radiative decay of eV-mass ALPs can contribute to cosmic background emissions at optical and infrared wavelengths. Line intensity mapping is a unique means of measuring the background emission at a given photon frequency. If ALPs constitute the abundance of cosmic dark matter, line intensity maps can correlate with large-scale structures probed by weak gravitational lensing effects in galaxy imaging surveys. We develop a theoretical framework to predict the cross correlation. We then explore potentiality of probing ALPs with the cross correlation in upcoming galaxy-imaging and spectral surveys. Assuming SPHEREx and the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST), we find that the cross correlation by the ALP decay can be greater than the astrophysical-line counterparts at wavelength of $\sim3000\, {\rm nm}$ for ALPs with a particle mass of $m_a\sim1\, \mathrm{eV}$ and a particle-to-two-photons coupling of $g_{aγγ}\sim1\times10^{-11}\, \mathrm{GeV}^{-1}$. We also predict that a null detection of the cross correlation can place a $2σ$-level upper bound of $g_{aγγ} \lower.5ex\hbox{$\; \buildrel < \over \sim \;$} 10^{-11}\, \mathrm{GeV}^{-1}$ for eV-mass ALPs, improving the current constraint by a factor of $\sim10$. We then make a forecast of expected constraints on ALP parameters in SPHEREx and LSST by Fisher analysis, providing a guideline of searching for the ALP decay with the large-scale structure data.

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arXiv2021

Subaru Hyper Suprime-Cam excavates colossal over- and under-dense structures over 360 deg2 out to z=1

Subaru Strategic Program with the Hyper-Suprime Cam (HSC-SSP) has proven to be successful with its extremely-wide area coverage in past years. Taking advantages of this feature, we report initial results from exploration and research of expansive over- and under-dense structures at $z=$ 0.3-1 based on the second Public Data Release where optical 5-band photometric data for $\sim$ eight million sources with $i<23$ mag are available over $\sim360$ square degrees. We not only confirm known superclusters but also find candidates of titanic over- and under-dense regions out to $z=1$. The mock data analysis suggests that the density peaks would involve one or more massive dark matter haloes ($>10^{14}$ M$_\odot$) of the redshift, and the density troughs tend to be empty of massive haloes over $>10$ comoving Mpc. Besides, the density peaks and troughs at $z<0.6$ are in part identified as positive and negative weak lensing signals respectively, in mean tangential shear profiles, showing a good agreement with those inferred from the full-sky weak lensing simulation. The coming extensive spectroscopic surveys will be able to resolve these colossal structures in three-dimensional space. The number density information over the entire survey field will be available as grid-point data on the website of the HSC-SSP data release (https://hsc.mtk.nao.ac.jp/ssp/data-release/).

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arXiv2021

A semi-analytic model of pairwise velocity distribution between dark matter halos

We study the probability distribution function (PDF) of relative velocity between two different dark matter halos (i.e. pairwise velocity) with a set of high-resolution cosmological $N$-body simulations. We investigate the pairwise velocity PDFs over a wide range of halo masses of $10^{12.5-15}\, h^{-1}M_{\odot}$ and redshifts of $0<z<1$. At a given set of masses, redshift and the separation length between two halos, our model requires three parameters to set the pairwise velocity PDF, whereas previous non-Gaussian models in the literature assume four or more free parameters. At the length scales of $r=5-40\, [h^{-1}\, \mathrm{Mpc}]$, our model predicts the mean and dispersion of the pairwise velocity for dark matter halos with their masses of $10^{12.5-13.5} \, [h^{-1}M_{\odot}]$ at $0.3 < z < 1$ with a 5%-level precision, while the model precision reaches a 20% level (mostly a 10% level) for other masses and redshifts explored in the simulations. We demonstrate that our model of the pairwise velocity PDF provides an accurate mapping of the two-point clustering of massive-galaxy-sized halos at the scales of $O(10)\, h^{-1}\mathrm{Mpc}$ between redshift and real space for a given real-space correlation function. For a mass-limited halo sample with their masses greater than $10^{13.5}\, h^{-1}M_{\odot}$ at $z=0.55$, our model can explain the monopole and quadropole moments of the redshift-space two-point correlations with a precision better than 5% at the scales of $5-40$ and $10-30\, h^{-1}\mathrm{Mpc}$, respectively. Our model of the pairwise velocity PDF will give a detailed explanation of statistics of massive galaxies at the intermediate scales in redshift surveys, including the non-linear redshift-space distortion effect in two-point correlation functions and the measurements of the kinematic Sunyaev-Zel'dovich effect.

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arXiv2021

Constraining Primordial Non-Gaussianity with Post-reconstructed Galaxy Bispectrum in Redshift Space

Galaxy bispectrum is a promising probe of inflationary physics in the early universe as a measure of primordial non-Gaussianity (PNG), whereas its signal-to-noise ratio is significantly affected by the mode coupling due to non-linear gravitational growth. In this paper, we examine the standard reconstruction method of linear cosmic mass density fields from non-linear galaxy density fields to de-correlate the covariance in redshift-space galaxy bispectra. In particular, we evaluate the covariance of the bispectrum for massive-galaxy-sized dark matter halos with reconstruction by using 4000 independent $N$-body simulations. Our results show that the bispectrum covariance for the post-reconstructed field approaches the Gaussian prediction at scale of $k<0.2\, h\, {\rm Mpc}^{-1}$. We also verify the leading-order PNG-induced bispectrum is not affected by details of the reconstruction with perturbative theory. We then demonstrate the constraining power of the post-reconstructed bispectrum for PNG at redshift of $\sim0.5$. Further, we perform a Fisher analysis to make a forecast of PNG constraints by galaxy bispectra including anisotropic signals. Assuming a massive galaxy sample in the SDSS Baryon Oscillation Spectroscopic Survey, we find that the post-reconstructed bispectrum can constrain the local-, equilateral- and orthogonal-types of PNG with $Δf_{\rm NL} \sim$13, 90 and 42, respectively, improving the constraints with the pre-reconstructed bispectrum by a factor of $1.3-3.2$. In conclusion, the reconstruction plays an essential role in constraining various types of PNG signatures with a level of $Δf_{\rm NL}<1$ from the galaxy bispectrum based on upcoming galaxy surveys.

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arXiv2020

Cosmological inference from emulator based halo model I: Validation tests with HSC and SDSS mock catalogs

We present validation tests of emulator-based halo model method for cosmological parameter inference, assuming hypothetical measurements of the projected correlation function of galaxies, $w_{\rm p}(R)$, and the galaxy-galaxy weak lensing, $Δ\!Σ(R)$, from the spectroscopic SDSS galaxies and the Hyper Suprime-Cam Year1 (HSC-Y1) galaxies. To do this, we use \textsc{Dark Emulator} developed in Nishimichi et al. based on an ensemble of $N$-body simulations, which is an emulation package enabling a fast, accurate computation of halo clustering quantities for flat-geometry $w$CDM cosmologies. Adopting the halo occupation distribution, the emulator allows us to obtain model predictions of $Δ\!Σ$ and $w_{\rm p}$ for the SDSS-like galaxies at a few CPU seconds for an input set of parameters. We present performance and validation of the method by carrying out Markov Chain Monte Carlo analyses of the mock signals measured from a variety of mock catalogs that mimic the SDSS and HSC-Y1 galaxies. We show that the halo model method can recover the underlying true cosmological parameters to within the 68\% credible interval, except for the mocks including the assembly bias effect (although we consider the unrealistically-large amplitude of assembly bias effect). Even for the assembly bias mock, we demonstrate that the cosmological parameters can be recovered {\it if} the analysis is restricted to scales $R\gtrsim 10~h^{-1}{\rm Mpc}$. We also show that, by using a single population of source galaxies to infer the relative strengths of $Δ\!Σ$ for multiple lens samples at different redshifts, the joint probes method allows for self-calibration of photometric redshift errors and multiplicative shear bias. Thus we conclude that the emulator-based halo model method can be safely applied to the HSC-Y1 dataset, achieving a precision of $σ(S_8)\simeq 0.04$.

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arXiv2020

Deep Learning for Line Intensity Mapping Observations: Information Extraction from Noisy Maps

Line intensity mapping (LIM) is a promising observational method to probe large-scale fluctuations of line emission from distant galaxies. Data from wide-field LIM observations allow us to study the large-scale structure of the universe as well as galaxy populations and their evolution. A serious problem with LIM is contamination by foreground/background sources and various noise contributions. We develop conditional generative adversarial networks (cGANs) that extract designated signals and information from noisy maps. We train the cGANs using 30,000 mock observation maps with assuming a Gaussian noise matched to the expected noise level of NASA's SPHEREx mission. The trained cGANs successfully reconstruct Hα emission from galaxies at a target redshift from observed, noisy intensity maps. Intensity peaks with heights greater than 3.5 σ noise are located with 60 % precision. The one-point probability distribution and the power spectrum are accurately recovered even in the noise-dominated regime. However, the overall reconstruction performance depends on the pixel size and on the survey volume assumed for the training data. It is necessary to generate training mock data with a sufficiently large volume in order to reconstruct the intensity power spectrum at large angular scales. Our deep-learning approach can be readily applied to observational data with line confusion and with noise.

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arXiv2020

Validating a minimal galaxy bias method for cosmological parameter inference using HSC-SDSS mock catalogs

We assess the performance of a perturbation theory inspired method for inferring cosmological parameters from the joint measurements of galaxy-galaxy weak lensing ($ΔΣ$) and the projected galaxy clustering ($w_{\rm p}$). To do this, we use a wide variety of mock galaxy catalogs constructed based on a large set of $N$-body simulations that mimic the Subaru HSC-Y1 and SDSS galaxies, and apply the method to the mock signals to address whether to recover the underlying true cosmological parameters in the mocks. We find that, as long as the appropriate scale cuts, $12$ and $8~h^{-1}{\rm Mpc}$ for $ΔΣ$ and $w_{\rm p}$ respectively, are adopted, a "minimal-bias" model using the linear bias parameter $b_1$ alone and the nonlinear matter power spectrum can recover the true cosmological parameters (here focused on $Ω_{\rm m}$ and $σ_8$) to within the 68% credible interval, for all the mocks we study including one in which an assembly bias effect is implemented. This is as expected if physical processes inherent in galaxy formation/evolution are confined to local, small scales below the scale cut, and thus implies that real-space observables have an advantage in filtering out the impact of small-scale nonlinear effects in parameter estimation, compared to their Fourier-space counterparts. In addition, we find that a theoretical template including the higher-order bias contributions such as nonlinear bias parameter $(b_2)$ does not improve the cosmological constraints, but rather leads to a larger parameter bias compared to the baseline $b_1$-method.

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arXiv2020

Weak lensing clusters from HSC survey first-year data: Mitigating the dilution effect of foreground and cluster member galaxies

We present a weak lensing cluster search using Hyper Suprime-Cam Subaru Strategic Program (HSC survey) first-year data. We pay special attention to the dilution effect of cluster member and foreground galaxies on weak lensing signals from clusters of galaxies; we adopt the globally normalized weak lensing estimator which is least affected by cluster member galaxies, and we select source galaxies by using photometric redshift information to mitigate the effect of foreground galaxies. We produce six samples of source galaxies with different low-$z$ galaxy cuts, construct weak lensing mass maps for each of the source sample, and search for high peaks in the mass maps that cover an effective survey area of $\sim$120 deg$^2$. We combine six catalogs of high peaks into a sample of cluster candidates which contains 124 high peaks with signal-to-noise ratios greater than five. We cross-match the peak sample with the public optical cluster catalog constructed from the same HSC survey data to identify cluster counterparts of the peaks. We find that 107 out of 124 peaks have matched clusters within 5 arcmin from peak positions. Among them, we define a sub-sample of 64 secure clusters that we use to examine dilution effects on our weak lensing cluster search. We find that source samples with the low-$z$ galaxy cuts mitigate the dilution effect on weak lensing signals of high-$z$ clusters ($z > 0.3$), and thus combining multiple peak catalogs from different source samples improves the efficiency of weak lensing cluster searches.

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arXiv2020

Deep learning for intensity mapping observations: Component extraction

Line intensity mapping (LIM) is an emerging observational method to study the large-scale structure of the Universe and its evolution. LIM does not resolve individual sources but probes the fluctuations of integrated line emissions. A serious limitation with LIM is that contributions of different emission lines from sources at different redshifts are all confused at an observed wavelength. We propose a deep learning application to solve this problem. We use conditional generative adversarial networks to extract designated information from LIM. We consider a simple case with two populations of emission line galaxies; H$\rmα$ emitting galaxies at $z = 1.3$ are confused with [OIII] emitters at $z = 2.0$ in a single observed waveband at 1.5 $\rmμ$m. Our networks trained with 30,000 mock observation maps are able to extract the total intensity and the spatial distribution of H$\rmα$ emitting galaxies at $z = 1.3$. The intensity peaks are successfully located with 74% precision. The precision increases to 91% when we combine the results of 5 networks. The mean intensity and the power spectrum are reconstructed with an accuracy of $\sim$10%. The extracted galaxy distributions at a wider range of redshift can be used for studies on cosmology and on galaxy formation and evolution.

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arXiv2020

Fitting the nonlinear matter bispectrum by the Halofit approach

We provide a new fitting formula of the matter bispectrum in the nonlinear regime calibrated by high-resolution cosmological $N$-body simulations of $41$ cold dark matter ($w$CDM, $w=$ constant) models around the Planck 2015 best-fit parameters. As the parameterization in our fitting function is similar to that in Halofit, our fitting is named BiHalofit. The simulation volume is sufficiently large ($> 10 \, {\rm Gpc}^3$) to cover almost all measurable triangle bispectrum configurations in the universe. The function is also calibrated using one-loop perturbation theory at large scales ($k<0.3 \, h \, {\rm Mpc}^{-1}$). Our formula reproduced the matter bispectrum to within $10 \, (15) \, \%$ accuracy in the Planck 2015 model at wavenumber $k< 3 \, (10) \, h \, {\rm Mpc}^{-1}$ and redshifts $z=0-3$. The other $40$ $w$CDM models obtained poorer fits, with accuracy approximating $20 \, \%$ at $k<3 \, h \, {\rm Mpc}^{-1}$ and $z=0-1.5$ (the deviation includes the $10 \, \%$-level sample variance of the simulations). We also provide a fitting formula that corrects the baryonic effects such as radiative cooling and active galactic nucleus feedback, using the latest hydrodynamical simulation IllustrisTNG. We demonstrate that our new formula more accurately predicts the weak-lensing bispectrum than the existing fitting formulas. This formula will assist current and future weak-lensing surveys and cosmic microwave background lensing experiments. Numerical codes of the formula are available, written in Python, C and Fortran.

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arXiv2020

Cross Correlation of the Extragalactic Gamma-ray Background with Thermal Sunyaev-Zel'dovich Effect in the Cosmic Microwave Background

Cosmic rays in galaxy clusters are unique probes of energetic processes operating with large-scale structures in the Universe. Precise measurements of cosmic rays in galaxy clusters are essential for improving our understanding of non-thermal components in the intracluster-medium (ICM) as well as the accuracy of cluster mass estimates in cosmological analyses. In this paper, we perform a cross-correlation analysis with the extragalactic gamma-ray background and the thermal Sunyaev-Zel'dovich (tSZ) effect in the cosmic microwave background. The expected cross-correlation signal would contain rich information about the cosmic-ray-induced gamma-ray emission in the most massive galaxy clusters at $z\sim0.1-0.2$. We analyze the gamma-ray background map with 8 years of data taken by the Large Area Telescope onboard Fermi satellite and the publicly available tSZ map by Planck. We confirm that the measured cross-correlation is consistent with a null detection, and thus it enables us to put the tightest constraint on the acceleration efficiency of cosmic ray protons at shocks in and around galaxy clusters. We find the acceleration efficiency must be below 5\% with a $2σ$ confidence level when the hydrostatic mass bias of clusters is assumed to be 30\%, and our result is not significantly affected by the assumed value of the hydrostatic mass bias. Our constraint implies that the non-thermal cosmic-ray pressure in the ICM can introduce only a $\le 3\%$ level of the hydrostatic mass bias, highlighting that cosmic rays alone do not account for the mass bias inferred by the Planck analyses. Finally, we discuss future detectability prospects of cosmic-ray-induced gamma rays from the Perseus cluster for the Cherenkov Telescope Array.

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arXiv2020

Cross-correlation of the thermal Sunyaev-Zel'dovich effect and weak gravitational lensing: Planck and Subaru Hyper Suprime-Cam first-year data

Cross-correlation analysis of the thermal Sunyaev-Zel'dovich (tSZ) effect and weak gravitational lensing (WL) provides a powerful probe of cosmology and astrophysics of the intra-cluster medium. We present the measurement of the cross-correlation of tSZ and WL from Planck and Subaru Hyper-Suprime Cam. The combination enables us to study cluster astrophysics at high redshift. We use the tSZ-WL cross-correlation and the tSZ auto-power spectrum measurements to place a tight constraint on the hydrostatic mass bias, which is a measure of the degree of non-thermal pressure support in galaxy clusters. With the prior on cosmological parameters derived from the analysis of the cosmic microwave background anisotropies by Planck and taking into account foreground contributions both in the tSZ auto-power spectrum and the tSZ-WL cross-correlation, the hydrostatic mass bias is estimated to be $26.9^{+8.9}_{-4.4} \%$ ($68\%$ C.L.), which is consistent with recent measurements by mass calibration techniques.

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arXiv2020

Constraining dark matter annihilation with HSC Low Surface Brightness Galaxies

Searches for dark matter annihilation signals have been carried out in a number of target regions such as the Galactic Center and Milky Way dwarf spheroidal galaxies (dSphs), among a few others. Here we propose low surface brightness galaxies (LSBGs) asnovel targets for the indirect detection of dark matter emission. In particular, LSBGs are known to have very large dark matter contents and be less contaminated by extragalactic gamma-ray sources (e.g., blazars) compared to star forming galaxies. We report on an analysis that uses eight LSBGs (detected by Subaru Hyper Suprime-Cam survey data) with known redshifts to conduct a search for gamma-ray emission at the positions of these new objects in Fermi Large Area Telescope data. We found no excesses of gamma-ray emission and set constraints on the dark matter annihilation cross-section. We exclude (at the 95% C.L.) dark matter scenarios predicting a cross-section higher than 10^-23[cm^3/s] for dark matter particles of mass 10 GeV self-annihilating in the b_b channel. Although this constraint is weaker than the ones reported in recent studies using other targets, we note that in the near future, the number of detections of new LSBGs will increase by a few orders of magnitude. We forecast that with the use of the full catalog of soon-to-be-detected LSBGs the constraint will reach cross-section sensitivities of ~ 3*10^-25 [cm^3/s] for dark matter particles with masses less than 10 GeV.

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arXiv2019

Probing Cosmology and Cluster Astrophysics with Multi-Wavelength Surveys I. Correlation Statistics

Upcoming multi-wavelength astronomical surveys will soon discover all massive galaxy clusters and provide unprecedented constraints on cosmology and cluster astrophysics. In this paper, we investigate the constraining power of the multi-band cluster surveys, through a joint analysis of three observables associated with clusters of galaxies, including thermal Sunyaev-Zel'dovich (tSZ) effect in cosmic microwave background (CMB), X-ray emission of ionized gas, and gravitational weak lensing effect of background galaxies by the cluster's gravitational potential. We develop a theoretical framework to predict and interpret two-point correlation statistics among the three observables using a semi-analytic model of intracluster medium (ICM) and halo-based approach. In this work, we show that the auto- and cross-angular power spectra in tSZ, X-ray and lensing statistics from upcoming missions (eROSITA, CMB-S4, and LSST) can help break the degeneracy between cosmology and ICM physics. These correlation statistics are less sensitive to selection biases, and are able to probe ICM physics in distant, faint and small clusters that are otherwise difficult to be detected individually. We show that the correlation statistics are able to provide cosmological constraints comparable to the conventional cluster abundance measurements, while constraining cluster astrophysics at the same time. Our results indicate that the correlation statistics can significantly enhance the scientific returns of upcoming multi-wavelength cluster surveys.

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