Search for: All records

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. 

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. 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 scatteredlight artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that lead to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.