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1. SPT-SZ MCMF: an extension of the SPT-SZ catalogue over the DES region

   https://doi.org/10.1093/mnras/stae1359  

   Klein, M.; Mohr, J_J; Bocquet, S.; Aguena, M.; Allen, S_W; Alves, O.; Ansarinejad, B.; Ashby, M_L_N; Bacon, D.; Bayliss, M.; et al (June 2024, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT We present an extension to a Sunyaev–Zel’dovich Effect (SZE) selected cluster catalogue based on observations from the South Pole Telescope (SPT); this catalogue extends to lower signal to noise than the previous SPT–SZ catalogue and therefore includes lower mass clusters. Optically derived redshifts, centres, richnesses, and morphological parameters together with catalogue contamination and completeness statistics are extracted using the multicomponent matched filter (MCMF) algorithm applied to the S/N > 4 SPT–SZ candidate list and the Dark Energy Survey (DES) photometric galaxy catalogue. The main catalogue contains 811 sources above S/N = 4, has 91  per cent purity, and is 95 per cent complete with respect to the original SZE selection. It contains in total 50 per cent more clusters and twice as many clusters above z = 0.8 in comparison to the original SPT-SZ sample. The MCMF algorithm allows us to define subsamples of the desired purity with traceable impact on catalogue completeness. As an example, we provide two subsamples with S/N > 4.25 and S/N > 4.5 for which the sample contamination and cleaning-induced incompleteness are both as low as the expected Poisson noise for samples of their size. The subsample with S/N > 4.5 has 98 per cent purity and 96 per cent completeness and is part of our new combined SPT cluster and DES weak-lensing cosmological analysis. We measure the number of false detections in the SPT-SZ candidate list as function of S/N, finding that it follows that expected from assuming Gaussian noise, but with a lower amplitude compared to previous estimates from simulations. 

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2. Exploring Data Corruption Inside SZ

   https://doi.org/10.1109/BigData55660.2022.10020891  

   Shan, Ruiwen; Calhoun, Jon C. (December 2022, 2022 IEEE International Conference on Big Data (Big Data))
3. The mass and galaxy distribution around SZ-selected clusters

   https://doi.org/10.1093/mnras/stab2505  

   Shin, T; Jain, B; Adhikari, S; Baxter, E J; Chang, C; Pandey, S; Salcedo, A; Weinberg, D H; Amsellem, A; Battaglia, N; et al (September 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We present measurements of the radial profiles of the mass and galaxy number density around Sunyaev–Zel’dovich (SZ)-selected clusters using both weak lensing and galaxy counts. The clusters are selected from the Atacama Cosmology Telescope Data Release 5 and the galaxies from the Dark Energy Survey Year 3 data set. With signal-to-noise ratio of 62 (45) for galaxy (weak lensing) profiles over scales of about 0.2–20 h−1 Mpc, these are the highest precision measurements for SZ-selected clusters to date. Because SZ selection closely approximates mass selection, these measurements enable several tests of theoretical models of the mass and light distribution around clusters. Our main findings are: (1) The splashback feature is detected at a consistent location in both the mass and galaxy profiles and its location is consistent with predictions of cold dark matter N-body simulations. (2) The full mass profile is also consistent with the simulations. (3) The shapes of the galaxy and lensing profiles are remarkably similar for our sample over the entire range of scales, from well inside the cluster halo to the quasilinear regime. We measure the dependence of the profile shapes on the galaxy sample, redshift, and cluster mass. We extend the Diemer & Kravtsov model for the cluster profiles to the linear regime using perturbation theory and show that it provides a good match to the measured profiles. We also compare the measured profiles to predictions of the standard halo model and simulations that include hydrodynamics. Applications of these results to cluster mass estimation, cosmology, and astrophysics are discussed. 

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4. Peculiar velocity estimation from kinetic SZ effect using deep neural networks

   https://doi.org/10.1093/mnras/stab1715  

   Wang, Yuyu; Ramachandra, Nesar; Salazar-Canizales, Edgar M; Feldman, Hume A; Watkins, Richard; Dolag, Klaus (July 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT The Sunyaev–Zel’dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the independent estimation of the optical depth. Images of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to determine its ability of estimating peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17 per cent compared to the analytical approach. 

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5. SZ–X-Ray Surface Brightness Fluctuations in the SPT-XMM Clusters

   https://doi.org/10.3847/1538-4357/adcd74  

   Romero, Charles E; Gaspari, Massimo; Schellenberger, Gerrit; Benson, Bradford A; Bleem, Lindsey E; Bulbul, Esra; Forman, William; Kraft, Ralph; Nulsen, Paul; Reichardt, Christian L; et al (May 2025, The Astrophysical Journal)

   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). 

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