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 dataset. With signal-to-noise of 62 (45) for galaxy (weak lensing) profiles over scales of about 0.2 − 20h−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.

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.

Short-pulse ion beams have been developed in recent years and now enable applications in materials science. A tunable flux of selected ions delivered in pulses of a few nanoseconds can affect the balance of defect formation and dynamic annealing in materials. We report results from color center formation in silicon with pulses of 900 keV protons. G-centers in silicon are near-infrared photon emitters with emerging applications as single-photon sources and for spin-photon qubit integration. G-centers consist of a pair of substitutional carbon atoms and one silicon interstitial atom and are often formed by carbon ion implantation and thermal annealing. Here, we report on G-center formation with proton pulses in silicon samples that already contained carbon, without carbon ion implantation or thermal annealing. The number of G-centers formed per proton increased when we increased the pulse intensity from 6.9 × 109 to 7.9 × 1010 protons/cm2/pulse, demonstrating a flux effect on G-center formation efficiency. We observe a G-center ensemble linewidth of 0.1 nm (full width half maximum), narrower than previously reported. Pulsed ion beams can extend the parameter range available for fundamental studies of radiation-induced defects and the formation of color centers for spin-photon qubit applications.

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