Trivedi, S. | Search NASA

DOE OSTI2022

(Cd,Mg)Te crystals for picosecond-response optical-to-x-ray radiation detectors

We demonstrate a photodetector sensitive to both optical and x-ray picosecond pulses based on our in-house grown cadmium magnesium telluride (Cd,Mg)Te single crystal. Specifically, we developed In-doped Cd0.96Mg0.04Te material and discuss its femtosecond optical photoresponse, as well as the detector performance, such as <100-pA dark current and up to 0.22-mA/W responsivity for 780-nm wavelength optical radiation. The detector exposed to Ti fluorescence (K alpha) x-ray pulses at 4.5 keV, generated by a free-electron laser beam with the central energy of 9.8 keV and <100 fs pulse width, exhibited readout-electronics-limited 200-ps full-width-at-half-maximum photoresponse, demonstrating that it is suitable for coarse timing in free-electron laser x-ray/optical femtosecond pump–probe spectroscopy applications.

Instruments & Instrumentation↗

DOE OSTI2021

DeepSZ: identification of Sunyaev–Zel’dovich galaxy clusters using deep learning

Galaxy clusters identified via the Sunyaev–Zel’dovich (SZ) effect are a key ingredient in multiwavelength cluster cosmology. In this paper, we present and compare three methods of cluster identification: the standard matched filter (MF) method in SZ cluster finding, a convolutional neural networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cut-out images of the sky as input, identifying the cut-out as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with signal-to-noise-ratio below the MF detection threshold. However, the CNN tends to mis-classify cut-out whose clusters are located near the edge of the cut-out, which can be mitigated with staggered cut-out. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters.

79 ASTRONOMY AND ASTROPHYSICS↗