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Masato Shirasaki

Publications and source records attributed to Masato Shirasaki.

At least 19 recordsLinked to original sources

Constraints on Annihilating Dark Matter from Gamma-Ray Background-Galaxy Shape Correlations: Model-independent Null Results and Moderate Template-based Signals

We revisit the cross-correlation between the unresolved $\gamma$-ray background and galaxy shapes to constrain the annihilation cross section of particle dark matter. Our analysis uses $\gamma$-ray photons from 14 years of observations with the Fermi Large Area Telescope (LAT), together with galaxy shape catalogs from the Dark Energy Survey Year 3 (DES Y3) and the Dark Energy Camera All Data Everywhere (DECADE) project, enabling us to probe cosmological large-scale signals over a common sky area of $\sim 12{,}000\,\mathrm{deg}^2$ shared by the $\gamma$-ray and galaxy data sets. In order to better access signals from large-scale structure, we employ a Fourier-space estimator for the cross-correlation in contrast to the previous DES Y3 analysis. We find that our measurements are consistent with a null detection in a model-independent $\chi^2$ test, while template-based analyses yield signals at the $\sim 3\sigma$ level. Our null results exclude an enhanced annihilation cross section for wino-like dark matter with a mass of $2-3$ TeV under a modest substructure boost factor of $\sim 30$ in Milky Way-sized halos. For larger boost factors of $\sim 100$, the constraints become significantly stronger and exclude the canonical thermal annihilation cross section $\langle \sigma v \rangle = 3 \times 10^{-26}\,\mathrm{cm}^3/\mathrm{s}$ for a $7-40$ GeV dark matter particle annihilating into $b\bar{b}$ or $\tau^{+}\tau^{-}$. The template-based analysis favors a power-law $\gamma$-ray energy dependence of the cross-correlation, but also indicates deviations from that expected based on the mean intensity of the unresolved $\gamma$-ray background around 100 GeV. We further consider decaying dark matter scenarios and derive $2\sigma$ lower limits on the particle lifetime of $\sim 10^{26}-10^{27}\,\mathrm{s}$, depending on the decay channel.

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Machine-learning applications for weak-lensing cosmology

This article reviews recent advances in the application of machine learning to weak-lensing cosmology. Weak gravitational lensing provides a unique and powerful probe of the total matter distribution in the Universe, independent of its physical state. By directly tracing the spatial distribution of otherwise invisible dark matter within the cosmic web, weak lensing has become a cornerstone for studying both the nature of dark matter and the physics governing large-scale structure formation. We begin by introducing the conventional estimators used to extract weak-lensing signals from modern galaxy-imaging surveys and by summarizing established methods for deriving cosmological information from these observables. We then discuss the limitations inherent in traditional analyses and outline how machine-learning techniques can mitigate these challenges. Finally, we explore future prospects for machine-learning-based approaches, highlighting their potential to further enhance the scientific return of current and upcoming weak-lensing datasets.

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Cross-correlating galaxies and cosmic dispersion measures: Constraints on the gas-to-halo mass relation from 2MASS galaxies and 133 localized fast radio bursts

We conduct a cross-correlation analysis between large-scale structures traced by the Two Micron All Sky Survey (2MASS) galaxy catalog and the cosmic dispersion measures of 133 localized fast radio bursts (FRBs). The cross-correlation signal is measured as a function of the comoving separation $R$ between 2MASS galaxies and background FRB sightlines, making full use of the available redshift information for both datasets. Our measurements are consistent with a null detection over the range $0.01 < R\, [h^{-1}\mathrm{Mpc}] < 1$. Using a halo-based model in which free-electron density profiles are drawn from the hydrodynamical simulation IllustrisTNG-300 (TNG300), we show that the null signal at $R \sim 0.01\, h^{-1}\mathrm{Mpc}$ is inconsistent with the TNG300 prediction. This discrepancy indicates that the hot-gas mass fraction in halos with masses of $10^{12-13}\, M_\odot$ hosting 2MASS galaxies must be lower than that predicted by TNG300. A simple phenomenological modification of the TNG300 model suggests that the hot-gas mass fraction in halos of $10^{12-13}\, M_\odot$ should be below $\sim 10\%$ of the global baryon fraction in the nearby universe, implying the need for stronger feedback in this mass range. Our constraints are consistent with those inferred from X-ray emission and Sunyaev-Zel'dovich measurements in galaxies, while providing a direct estimate of the hot-gas mass fraction that does not rely on electron-temperature measurements. These results demonstrate that galaxy-FRB cross correlations offer a powerful probe of feedback processes in galaxy formation.

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Denoising weak lensing mass maps with diffusion model and generative adversarial network

The matter distribution of the Universe can be mapped through the weak gravitational lensing (WL) effect: small distortions of the shapes of distant galaxies, which reflects the inhomogeneity of the cosmic density field. The most dominant contaminant in the WL effect is the shape noise; the signal is diluted due to the finite number of source galaxies. In order to explore the full potential of WL measurements, sharpening the signal by removing the shape noise from the observational data, i.e., WL denoising, is a pressing issue. Machine learning approaches, in particular, deep generative models, have proven effective at the WL denoising task. We implement a denoising model based on the diffusion model (DM) and conduct systematic in-depth comparisons with generative adversarial networks (GANs), which have been applied in previous works for WL denoising. Utilizing the large suite of mock simulations of WL observations, we demonstrate that DM surpasses GAN in the WL denosing task in multiple aspects: (1) the training process is more stable, (2) taking the average of multiple samples from DM can robustly reproduce the true signal, and (3) DM can recover various statistics with higher accuracy.

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Measurement of angular cross-correlation between the cosmological dispersion measure and the thermal Sunyaev--Zeldovich effect

The dispersion measures (${\rm DMs}$) from fast radio bursts (FRBs) and the thermal Sunyaev--Zeldovich (tSZ) effect probe the free-electron density and thermal pressure, respectively, in the intergalactic medium (IGM) and the intervening galaxies and clusters. Their combination enables disentangling the gas density and temperature. In this work, we present the first detection of an angular cross-correlation between the ${\rm DMs}$ and the Compton $y$ parameter of the tSZ effect. The theoretical expectation is calculated using the halo model $\texttt{HMx}$, calibrated with hydrodynamic simulations. The observational cross-correlation is measured over angular separations of $1^\prime$--$1000^\prime$ using the ${\rm DMs}$ from $133$ localized FRBs and the $y$-maps from the Planck satellite and the Atacama Cosmology Telescope (ACT). We detect a positive correlation with amplitudes of $\mathcal{A}=2.01 \pm 0.50$ ($4.0 \sigma$) for Planck and $\mathcal{A}=1.23 \pm 0.82$ ($1.5 \sigma$) for ACT, where $\mathcal{A}=1$ corresponds to the theoretical prediction of the Planck 2018 $\Lambda$CDM cosmology. Assuming an isothermal gas, the measured amplitude implies an average electron temperature of $\approx 2 \times 10^7 \, {\rm K}$. The correlation is highly sensitive to the matter clustering parameter $\sigma_8$ and to baryon feedback, and its dependence on other cosmological and astrophysical parameters -- such as the ionized fraction and the Hubble constant -- differs from that of the ${\rm DM}$ alone. This suggests that future joint analyses of the ${\rm DMs}$ and the tSZ effect could help break degeneracies among these parameters.

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Hyper Suprime-Cam Y3 results: photo-$z$ bias calibration with lensing shear ratios and cosmological constraints from cosmic shear

We present an independent calibration of the photometric redshift (photo-$z$) distributions for source galaxies in the HSC-Y3 weak lensing survey using small-scale galaxy-galaxy lensing. By measuring the tangential shear around spectroscopic lens galaxies from GAMA, SDSS, and DESI, divided into fifteen narrow redshift bins, we compute shear ratios that are sensitive to the mean redshift of source galaxies. Using a blinded analysis, we derive constraints on the photo-$z$ bias parameters in source bins 2, 3 and 4, achieving signal-to-noise ratios of 59, 75, and 62, respectively. Our constraints for $\Delta z_2$, $\Delta z_3$ and $\Delta z_4$ are consistent with those from HSC-Y3 cosmic shear modeling. We observe a mild shift in the $\Delta z_3$--$\Delta z_4$ plane due to the heterogeneous depth of the lens sample, which disappears when using only DESI-DR1 lenses. Combining shear-ratio measurements with cosmic shear data, we obtain joint constraints on cosmological parameters: $\Omega_{\rm m} = 0.304_{-0.029}^{+0.03}$ and $S_8 = 0.773_{-0.031}^{+0.031}$, consistent with cosmic shear-only results. This work demonstrates the utility of small-scale lensing as an independent probe for calibrating photometric redshift bias in weak lensing cosmology.

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First Constraints from Marked Angular Power Spectra with Subaru Hyper Suprime-Cam Survey First-Year Data

We present the first application of marked angular power spectra to weak lensing data, using maps from the Subaru Hyper Suprime-Cam Year 1 (HSC-Y1) survey. Marked convergence fields, constructed by weighting the convergence field with non-linear functions of its smoothed version, are designed to encode higher-order information while remaining computationally tractable. Using simulations tailored to the HSC-Y1 data, we test three mark functions that up- or down-weight different density environments. Our results show that combining multiple types of marked auto- and cross-spectra improves constraints on the clustering amplitude parameter $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}$ by $\approx$43\% compared to standard two-point power spectra. When applied to the HSC-Y1 data, this translates into a constraint on $S_8 = 0.807\pm 0.024$. We assess the sensitivity of the marked power spectra to systematics, including baryonic effects, intrinsic alignment, photometric redshifts, and multiplicative shear bias. These results demonstrate the promise of marked statistics as a practical and powerful tool for extracting non-Gaussian information from weak lensing surveys.

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Modelling Galaxy Clustering and Tomographic Galaxy-Galaxy Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model and Redshift Self-Calibration

The combination of galaxy-galaxy weak lensing and galaxy clustering is a powerful probe of the cosmological model, and exploration of how to best model and extract this information from the signals is essential. We present the measurement of the galaxy-galaxy weak lensing signals using the SDSS DR11 spectroscopic galaxies as lens galaxies, and the HSC Y3 shear catalog as source galaxies, binned into four tomographic bins by their photometric redshift. The SDSS DR11 galaxies, with a redshift range $0.15<z<0.7$, are binned into three redshift bins, each as a probe for measuring the projected correlation function, $w_p(R_p)$. We measure the galaxy-galaxy lensing signal $\Delta \Sigma (R_p)$ in 12 lens-source bin pairs and show that there is no evidence for significant systematic biases in the measurement with null testing. We combine our $w_p(R_p)$ and $\Delta \Sigma (R_p)$ ($2\times2$pt) data vectors and perform likelihood inference with a flat $\Lambda$CDM model. For $\Delta \Sigma (R_p)$, we extend the lower limit of the scale cut compared to previous HSC Y3 analyses to $2 h^{-1}$Mpc by including a point-mass correction term in addition to the minimal bias model. We present various tests to validate our model and provide extended consistency tests. In the $\Lambda$CDM context, our fiducial model yields $S_8 = 0.804^{+0.051}_{-0.051}$. The $2\times2$pt data vector provides redshift parameter constraints for the third and fourth redshift bins $\Delta z_3 = -0.079^{+0.074}_{-0.084}$, and $\Delta z_4 = -0.203^{+0.167}_{-0.206}$, which is consistent with results from the previous tomographic cosmic shear studies, and serves as the foundation for a future $3\times 2$pt analysis.

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Cosmology and Source Redshift Constraints from Galaxy Clustering and Tomographic Weak Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model

The combination of galaxy clustering and weak lensing is a powerful probe of the cosmology model. We present a joint analysis of galaxy clustering and weak lensing cosmology using SDSS data as the tracer of dark matter (lens sample) and the HSC Y3 dataset as source galaxies. The analysis divides HSC Y3 galaxies into four tomographic bins for both galaxy-galaxy lensing and cosmic shear measurements, and employs a point-mass correction model to utilize galaxy-galaxy lensing signals down to 2$h^{-1}$Mpc, extending up to 70$h^{-1}$Mpc. These strategies enhance the signal-to-noise ratio of the galaxy-galaxy lensing data vector. Using a flat $\Lambda$CDM model, we find $S_8 = 0.780^{+0.029}_{-0.030}$, and using a $w$CDM model, we obtain $S_8 = 0.756^{+0.038}_{-0.036}$ with $w = -1.176^{+0.310}_{-0.346}$. We apply uninformative priors on the redshift mean-shift parameters for the third and fourth tomographic bins. Leveraging the self-calibration power of tomographic weak lensing, we measure $\Delta z_3 = -0.112^{+0.046}_{-0.049}$ and $\Delta z_4 = -0.185^{+0.071}_{-0.081}$, in agreement with previous HSC Y3 results. This demonstrates that weak lensing self-calibration can achieve redshift constraints comparable to other methods such as photometric and clustering redshift calibration.

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Denoising weak lensing mass maps with diffusion model: systematic comparison with generative adversarial network

Removing the shape noise from the observed weak lensing field, i.e., denoising, enhances the potential of WL by accessing information at small scales where the shape noise dominates without denoising. We utilise two machine learning (ML) models for denosing: generative adversarial network (GAN) and diffusion model (DM). We evaluate the performance of denosing with GAN and DM utilising the large suite of mock WL observations, which serve as the training and test data sets. We apply denoising to 1,000 noisy mass maps with GAN and DM models trained with 39,000 mock observations. Both models can fairly well reproduce the true convergence map on large scales. Then, we measure cosmological statistics: power spectrum, bispectrum, one-point probability distribution function, peak and minima counts, and scattering transform coefficients. We find that DM outperforms GAN in almost all considered statistics and recovers the correct statistics down to small scales. For example, the angular power spectrum can be recovered with DM up to multipoles $\ell \lesssim 6000$ while the noise power spectrum dominates from $\ell \simeq 2000$. We also conduct stress tests on the trained model; denoising the maps with different characteristics, e.g., different source redshifts, from the training data. The performance degrades at small scales, but the statistics can still be recovered at large scales. Though the training of DM is more computationally demanding compared with GAN, there are several advantages: numerically stable training, higher performance in the reconstruction of cosmological statistics, and sampling multiple realisations once the model is trained. It has been known that DM can generate higher-quality images in real-world problems than GAN, the superiority has been confirmed as well in the WL denoising problem.

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Baryon Pasting the Uchuu Lightcone Simulation

We present the Baryon Pasted (BP) X-ray and thermal Sunyaev-Zel'dovich (tSZ) maps derived from the half-sky Uchuu Lightcone simulation. These BP-Uchuu maps are constructed using more than $75$ million dark matter halos with masses $M_{500c} \geq 10^{13} M_\odot$ within the redshift range $0 \leq z \leq 2$. A distinctive feature of our BP-Uchuu Lightcone maps is their capability to assess the influence of both extrinsic and intrinsic scatter caused by triaxial gaseous halos and internal gas characteristics, respectively, at the map level. We show that triaxial gas drives substantial scatter in X-ray luminosities of clusters and groups, accounting for nearly half of the total scatter in core-excised measurements. Additionally, scatter in the thermal pressure and gas density profiles of halos enhances the X-ray and SZ power spectra, leading to biases in cosmological parameter estimates. These findings are statistically robust due to the extensive sky coverage and large halo sample in the BP-Uchuu maps. The BP-Uchuu maps are publicly available on https://app.globus.org/file-manager?origin_id=cf8dadb7-b6e9-4e2c-abc1-0813877efc13 .

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Cosmology and Astrophysics with the Diffuse eRASS1 X-ray Angular Power Spectrum

The recent tension in the value of the cosmological parameter $S_8 \equiv \sigma_8(\Omega_M/0.3)^{1/2}$, which represents the amplitude of the matter density fluctuations of the universe, has not been resolved. In this work, we present constraints on $S_8$ with the X-ray angular power spectra of clusters and groups measured with the half-sky map from the eROSITA All Sky Survey data release 1 (eRASS1). Thanks to the extensive sky coverage of eRASS1, it achieves unprecedented precision compared to previous power spectrum measurements. Using a well-calibrated, physical halo gas model that includes astrophysics of feedback and non-thermal pressure support, we obtain $S_8 = 0.80^{+0.02}_{-0.01}$ with 1$\sigma$ uncertainty that is competitive against other cosmological probes. Our derived $S_8$ value is smaller than the primary CMB measurements from {\em Planck}, but still consistent to within $1\sigma$. We also obtain constraints on the astrophysics of feedback, non-thermal pressure, equation of state in cluster cores, and outer boundaries of gas in clusters and groups. We discuss how additional X-ray observations, and cosmological surveys in microwave and optical, will further improve the cosmological constraints with the angular power spectrum. Our work demonstrates that the angular power spectrum of clusters and groups is a promising probe of both cosmology and astrophysics.

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Cosmological constraints using Minkowski functionals from the first year data of the Hyper Suprime-Cam

We use Minkowski functionals to analyse weak lensing convergence maps from the first-year data release of the Subaru Hyper Suprime-Cam (HSC-Y1) survey. Minkowski functionals provide a description of the morphological properties of a field, capturing the non-Gaussian features of the Universe matter-density distribution. Using simulated catalogs that reproduce survey conditions and encode cosmological information, we emulate Minkowski functionals predictions across a range of cosmological parameters to derive the best-fit from the data. By applying multiple scales cuts, we rigorously mitigate systematic effects, including baryonic feedback and intrinsic alignments. From the analysis, combining constraints of the angular power spectrum and Minkowski functionals, we obtain $S_8 \equiv \sigma_8\sqrt{\Omega_{{\rm m}}/0.3} = {0.808}_{-0.046}^{+0.033}$ and $\Omega_{\rm m} = {0.293}_{-0.043}^{+0.157}$. These results represent a $40\%$ improvement on the $S_8$ constraints compared to using power spectrum only. \newtext{Minkowski functionals results are consistent with other two-point, and higher order statistics constraints using the same data, being in agreement with CMB results from the Planck $S_8$ measurements. Our study demonstrates the power of Minkowski functionals beyond two-point statistics to constrain and break the degeneracy between $\Omega_{\rm m}$ and $\sigma_8$.

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Cosmology from HSC Y1 Weak Lensing with Combined Higher-Order Statistics and Simulation-based Inference

We present cosmological constraints from weak lensing with the Subaru Hyper Suprime-Cam (HSC) first-year (Y1) data, using a simulation-based inference (SBI) method. % We explore the performance of a set of higher-order statistics (HOS) including the Minkowski functionals, counts of peaks and minima, and the probability distribution function and compare them to the traditional two-point statistics. The HOS, also known as non-Gaussian statistics, can extract additional non-Gaussian information that is inaccessible to the two-point statistics. We use a neural network to compress the summary statistics, followed by an SBI approach to infer the posterior distribution of the cosmological parameters. We apply cuts on angular scales and redshift bins to mitigate the impact of systematic effects. Combining two-point and non-Gaussian statistics, we obtain $S_8 \equiv \sigma_8 \sqrt{\Omega_m/0.3} = 0.804_{-0.040}^{+0.041}$ and $\Omega_m = 0.344_{-0.090}^{+0.083}$, similar to that from non-Gaussian statistics alone. These results are consistent with previous HSC analyses and Planck 2018 cosmology. Our constraints from non-Gaussian statistics are $\sim 25\%$ tighter in $S_8$ than two-point statistics, where the main improvement lies in $\Omega_m$, with $\sim 40$\% tighter error bar compared to using the angular power spectrum alone ($S_8 = 0.766_{-0.056}^{+0.054}$ and $\Omega_m = 0.365_{-0.141}^{+0.148}$). We find that, among the non-Gaussian statistics we studied, the Minkowski functionals are the primary driver for this improvement. Our analyses confirm the SBI as a powerful approach for cosmological constraints, avoiding any assumptions about the functional form of the data's likelihood.

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Masses of Sunyaev-Zel'dovich Galaxy Clusters Detected by The Atacama Cosmology Telescope: Stacked Lensing Measurements with Subaru HSC Year 3 data

We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel'dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a $5\sigma$-level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of $0.1 < R\, [h^{-1}\mathrm{Mpc}] < 100$ in each of three cluster redshift bins, $0.092<z\le0.445$, $0.445<z\le0.695$, and $0.695<z\le1.180$, with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are $14.6$, $12.0$, and $6.6$, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample ($2.1-2.4\times10^{14}\, h^{-1}M_\odot$), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of $1.3 \pm 0.2$, $1.6 \pm 0.2$, and $1.6 \pm 0.3$ ($68\%$) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a $1\sigma$-level difference in our parameter inferences.

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Cosmological constraints from weak lensing scattering transform using HSC Y1 data

As weak lensing surveys go deeper, there is an increasing need for reliable characterization of non-Gaussian structures at small angular scales. Here we present the first cosmological constraints with weak lensing scattering transform, a statistical estimator that combines efficiency, robustness, and interpretability. With the Hyper Suprime-Cam survey (HSC) year 1 data, we obtain $\Omega_\text{m}=0.29_{-0.03}^{+0.04}$, $S_8\equiv \sigma_8(\Omega_\text{m}/0.3)^{0.5}=0.83\pm0.02$, and intrinsic alignment strength $A_\text{IA}=1.0\pm0.4$ through simulation-based forward modeling. Our constraints are consistent with those derived from Planck. The error bar of $\Omega_\text{m}$ is 2 times tighter than that obtained from the power spectrum when the same scale range is used. This constraining power is on par with that of convolutional neural networks, suggesting that further investment in spatial information extraction may not yield substantial benefits. We also point out an internal tension of $S_8$ estimates linked to a redshift bin around z ~ 1 in the HSC data. We found that discarding that bin leads to a consistent decrease of $S_8$ from 0.83 to 0.79, for all statistical estimators. We argue that photometric redshift estimation is now the main limitation in the estimation of $S_8$ using HSC. This limitation is likely to affect other ground-based weak lensing surveys reaching redshifts greater than one. Alternative redshift estimation techniques, like clustering redshifts, may help alleviate this limitation.

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Exploring the baryonic effect signature in the Hyper Suprime-Cam Year 3 cosmic shear two-point correlations on small scales: the $S_8$ tension remains present

The baryonic feedback effect is considered as a possible solution to the so-called $S_8$ tension indicated in cosmic shear cosmology. The baryonic effect is more significant on smaller scales, and affects the cosmic shear two-point correlation functions (2PCFs) with different scale- and redshift-dependencies from those of the cosmological parameters. In this paper, we use the Hyper Suprime-Cam Year 3 (HSC-Y3) data to measure the cosmic shear 2PCFs ($\xi_{\pm}$) down to 0.28 arcminutes, taking full advantage of the high number density of source galaxies in the deep HSC data, to explore a possible signature of the baryonic effect. While the published HSC analysis used the cosmic shear 2PCFs on angular scales, which are sensitive to the matter power spectrum at $k\lesssim 1~h{\rm Mpc}^{-1}$, the smaller scale HSC cosmic shear signal allows us to probe the signature of matter power spectrum up to $k\simeq 20~h{\rm Mpc}^{-1}$. Using the accurate emulator of the nonlinear matter power spectrum, DarkEmulator2, we show that the dark matter-only model can provide an acceptable fit to the HSC-Y3 2PCFs down to the smallest scales. In other words, we do not find any clear signature of the baryonic effects or do not find a systematic shift in the $S_8$ value with the inclusion of the smaller-scale information as would be expected if the baryonic effect is significant. Alternatively, we use a flexible 6-parameter model of the baryonic effects, which can lead to both enhancement and suppression in the matter power spectrum compared to the dark matter-only model, to perform the parameter inference of the HSC-Y3 2PCFs. We find that the small-scale HSC data allow only a fractional suppression of up to 5 percent in the matter power spectrum at $k\sim 1~h{\rm Mpc}^{-1}$, which is not sufficient to reconcile the $S_8$ tension.

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Impact of baryonic feedback on HSC Y1 weak lensing non-Gaussian statistics

Baryonic feedback is a major systematic in weak lensing cosmology. Its most studied effect is the suppression of the lensing power spectrum, a second-order statistic, on small scales. Motivated by the growing interest in statistics beyond the second order, we investigate the effect of baryons on lensing non-Gaussian statistics and the resulting biases in the matter clustering amplitude $S_8 = \sigma_8\sqrt{\Omega_m/0.3}$. We focus on the Subaru Hyper Suprime-Cam Year 1 (HSC-Y1) data which, with its high source number density, closely resembles those expected from the upcoming Euclid and Rubin LSST. We study four non-Gaussian statistics -- peak counts, minimum counts, the probability distribution function, and the scattering transform -- in addition to the usual power spectrum. We first estimate the biases in $S_8$ using mock observations built from the IllustrisTNG and BAHAMAS hydrodynamical simulations and theoretical models built from dark matter-only simulations. We find up to $1\sigma$ bias in $S_8$ when the smallest scales (2 arcmin) and the highest feedback level are considered. We then analyze the HSC-Y1 data and compare the $S_8$ obtained for each statistic with different smoothing scales or scale cuts. As we expect that baryons mostly affect the small scales, comparing the results obtained from including and excluding small scales can indicate the level of impact from baryons. With HSC data, we find only minor ($\leq0.5\sigma$) differences in $S_8$ for all statistics, even when considering very small scales (2 arcmin). Our results suggest that the effect of baryons is insignificant at the level of HSC-Y1 down to 2~arcmin for all statistics examined here, or it is canceled by other scale-dependent systematics.

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