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Galaxy cluster mergers are excellent laboratories for studying a wide variety of different physical phenomena. An example of such a cluster system is the distant SPT-CLJ2228-5828 merger located atz ≈ 0.77. Previous analyses via the thermal Sunyaev-Zeldovich effect and weak lensing (WL) data suggested that the system was potentially a dissociative cluster post-merger, similar to the Bullet cluster. In this work, we perform an X-ray and optical follow-up analysis of this rare system. We used new deepXMM-Newtondata to study the hot gas in X-rays in great detail, spectroscopicGeminidata to precisely determine the redshift of the two mass concentrations, and newHubbleSpace Telescope data to improve the total mass estimates of the two components. We find that SPT-CLJ2228-5828 constitutes a pre-merging double cluster system instead of a post-merger as previously thought. The merging process of the two clusters has started, with their gas on the outskirts colliding with a ∼22° −27° on the plane of the sky. Both clusters have a similar radius ofR₅₀₀ ∼ 700 kpc, with the two X-ray emission peaks separated by ≈1 Mpc (2.1′). We fully characterized the surface brightness, gas density, temperature, pressure, and entropy profiles of the two merging clusters for their undisturbed non-interacting side. The two systems have very similar X-ray properties, with a moderate cluster mass ofM_tot ∼ (2.1 − 2.4)×10¹⁴ M_⊙according to X-ray mass proxies. Both clusters show good agreement with known X-ray scaling relations when their merging side is ignored. The WL mass estimate of the western cluster agrees well with the X-ray-based mass, whereas the eastern cluster is surprisingly only marginally detected from its WL signal. A gas bridge with ≈333 kpc length connecting the two merging halos is detected at a 5.8σlevel. The baryon overdensity of the excess gas (not associated with the cluster gas) isδ_b ∼ (75 − 320) across the length of the bridge, and its gas mass isM_gas ∼ 1.4 × 10¹² M_⊙. The gas density and temperature jumps at ∼10⁻³cm⁻³and ∼5.5 keV, respectively, are also found across the gas bridge, revealing the existence of a weak shock front with a Mach number ℳ ∼ 1.1. The gas pressure and entropy also increase at the position of the shock front. We estimate the age of the shock front to be ≲100 Myr and its kinetic energy ∼2.4 × 10⁴⁴erg s⁻¹. SPT-CLJ2228-5828 is the first such high-zpre-merger with a gas bridge and a shock front, consisting of similarly sized clusters, to be studied in X-rays. 

Silk, a popular biomaterial is used as a greener alternative of toxic reducing agent in biocompatible graphene synthesis. However, silk often forms gel uncontrollably due to its heavy chain molecular weight, and faces significant challenges in the reduction, stabilization, and dispersion process of graphene. In this contribution, we report a rapid chemical synthesis approach for a low molecular weight silk-inspired polymer via ring opening and microwave-assisted Diels-Alder aided step-growth polymerizations. This synthetic polymer with periodic sequences of hydrophilic and hydrophobic moieties not only reduces graphene oxide (GO) efficiently but enhances the dispersibility of hydrophobic reduced GO (rGO) in aqueous media. 

Randomized clinical trials have been the mainstay of clinical research, but are prohibitively expensive and subject to increasingly difficult patient recruitment. Recently, there is a movement to use real-world data (RWD) from electronic health records, patient registries, claims data and other sources in lieu of or supplementing controlled clinical trials. This process of combining information from diverse sources calls for inference under a Bayesian paradigm. We review some of the currently used methods and a novel non-parametric Bayesian (BNP) method. Carrying out the desired adjustment for differences in patient populations is naturally done with BNP priors that facilitate understanding of and adjustment for population heterogeneities across different data sources. We discuss the particular problem of using RWD to create a synthetic control arm to supplement single-arm treatment only studies. At the core of the proposed approach is the model-based adjustment to achieve equivalent patient populations in the current study and the (adjusted) RWD. This is implemented using common atoms mixture models. The structure of such models greatly simplifies inference. The adjustment for differences in the populations can be reduced to ratios of weights in such mixtures. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’. 

We present constraints on the $f\left(R\right)$gravity model using a sample of 1005 galaxy clusters in the redshift range 0.25–1.78 that have been selected through the thermal Sunyaev-Zel’dovich effect from South Pole Telescope data and subjected to optical and near-infrared confirmation with the multicomponent matched filter algorithm. We employ weak gravitational lensing mass calibration from the Dark Energy Survey Year 3 data for 688 clusters at $z<0.95$and from the Hubble Space Telescope for 39 clusters with $0.6<z<1.7$. Our cluster sample is a powerful probe of $f\left(R\right)$gravity, because this model predicts a scale-dependent enhancement in the growth of structure, which impacts the halo mass function (HMF) at cluster mass scales. To account for these modified gravity effects on the HMF, our analysis employs a semianalytical approach calibrated with numerical simulations. Combining calibrated cluster counts with primary cosmic microwave background temperature and polarization anisotropy measurements from the Planck 2018 release, we derive robust constraints on the $f\left(R\right)$parameter $f_{R0}$. Our results, ${\log }_{10}\left|f_{R0}\right|<-5.32$at the 95% credible level, are the tightest current constraints on $f\left(R\right)$gravity from cosmological scales. This upper limit rules out $f\left(R\right)$-like deviations from general relativity that result in more than a $\sim 20\%$enhancement of the cluster population on mass scales $M_{200\mathrm{c}}>3\times {10}^{14}{M}_{\odot }$. Published by the American Physical Society2025 

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements ( $3\times 2\mathrm{pt}$) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\mathrm{\Lambda }$cold dark matter ( $\mathrm{\Lambda }\mathrm{CDM}$) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure ${\mathrm{\Omega }}_{\mathrm{m}}=0.300\pm 0.017$and ${\sigma }_8=0.797\pm 0.026$. Compared to constraints from primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ( $1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv {\sigma }_8({\mathrm{\Omega }}_{\mathrm{m}}/0.3{)}^{0.5}=0.796\pm 0.013$which is lower than the measurement at the $1.6\sigma$level. The combined SPT cluster, DES $3\times 2\mathrm{pt}$, and datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit $\sum m_{\nu }<0.25\mathrm{eV}$on the sum of neutrino masses. Assuming a $w\mathrm{CDM}$model, we constrain the dark energy equation of state parameter $w=-1.15_{-0.17}^{+0.23}$and when combining with primary CMB anisotropies, we recover $w=-1.20_{-0.09}^{+0.15}$, a $1.7\sigma$difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology and our analysis paves the way for upcoming joint analyses of next-generation datasets. Published by the American Physical Society2025