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Abstract The hot plasma in galaxy clusters, the intracluster medium, is expected to be shaped by subsonic turbulent motions, which are key for heating, cooling, and transport mechanisms. The turbulent motions contribute to the nonthermal pressure, which, if not accounted for, consequently imparts a hydrostatic mass bias. Accessing information about turbulent motions is thus of major astrophysical and cosmological interest. Characteristics of turbulent motions can be indirectly accessed through surface brightness fluctuations. This study expands on our pilot investigations of surface brightness fluctuations in the Sunyaev–Zel’dovich and in X-ray data by examining, for the first time, a large sample of 60 clusters using both SPT-SZ and XMM-Newton data and spans the redshift range 0.2 < z < 1.5, thus constraining the respective pressure and density fluctuations within 0.6R₅₀₀. We deem density fluctuations to be of sufficient quality for 32 clusters, finding mild correlations between the peak of the amplitude spectra of density fluctuations and various dynamical parameters. We infer turbulent velocities from density fluctuations with an average Mach number ${\mathcal{M}}_{\text{3D}}=0.52\pm 0.14$, in agreement with numerical simulations. For clusters with inferred turbulent Mach numbers from fluctuations in both pressure, ${\mathcal{M}}_{\text{P}}$, and density, ${\mathcal{M}}_{\rho }$, we find broad agreement between ${\mathcal{M}}_{\text{P}}$and ${\mathcal{M}}_{\rho }$. Our results suggest either a bimodal or a skewed unimodal Mach number distribution, with the majority of clusters being turbulence-dominated (subsonic) while the remainder are shock-dominated (supersonic). 

Abstract We present joint South Pole Telescope and XMM-Newton observations of eight massive galaxy clusters (0.8–2 × 10¹⁵M_⊙) spanning a redshift range of 0.16–0.35. Employing a novel Sunyaev–Zel’dovich + X-ray fitting technique, we effectively constrain the thermodynamic properties of these clusters out to the virial radius. The resulting best-fit electron density, deprojected temperature, and deprojected pressure profiles are in good agreement with previous observations of massive clusters. For the majority of the cluster sample (five out of eight clusters), the entropy profiles exhibit a self-similar behavior near the virial radius. We further derive hydrostatic mass, gas mass, and gas fraction profiles for all clusters up to the virial radius. Comparing the enclosed gas fraction profiles with the universal gas fraction profile, we obtain nonthermal pressure fraction profiles for our cluster sample at  >0.5R₅₀₀, demonstrating a steeper increase betweenR₅₀₀andR₂₀₀that is consistent with the hydrodynamical simulations. Our analysis yields nonthermal pressure fraction ranges of 8%–28% (median: 15% ± 11%) atR₅₀₀and 21%–35% (median: 27% ± 12%) atR₂₀₀. Notably, weak-lensing mass measurements are available for only four clusters in our sample, and our recovered total cluster masses, after accounting for nonthermal pressure, are consistent with these measurements. 

Abstract Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS₂, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co$${}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$ ${}_S^0$) and fabricate it with scanning tunneling microscopy (STM). The Co$${}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$ ${}_S^0$electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects. 

Gene expression profiles are correlated to disease phenotypes in resistant and susceptible corals. 

We use natural language processing (NLP) to train an automated scoring model to assess students’ reasoning on how to slow climate change. We use the insights from scoring over 1000 explanations to design a knowledge integration intervention and test it in three classrooms. The intervention supported students to distinguish relevant evidence, improving connections between ideas in a revised explanation. We discuss next steps for using the NLP model to support teachers and students in classrooms.