ABSTRACT
New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within the detector hardware will reduce the data volumes generated, thereby reducing material cost, power, and data management requirements. Several solutions are already being developed both on the hardware and on the software side to facilitate the use of machine learning as a real-time data analysis solution.
Subject(s)
Machine Learning , Software , PhotonsABSTRACT
The continuing improvement in quantum efficiency (above 90% for single visible photons), reduction in noise (below 1 electron per pixel), and shrink in pixel pitch (less than 1 µm) enable billion-pixel x-ray cameras (BiPC-X) based on commercial complementary metal-oxide-semiconductor (CMOS) imaging sensors. We describe BiPC-X designs and prototype construction based on flexible tiling of commercial CMOS imaging sensors with millions of pixels. Device models are given for direct detection of low energy x rays (<10 keV) and indirect detection of higher energies using scintillators. Modified Birks's law is proposed for light yield non-proportionality in scintillators as a function of x-ray energy. Single x-ray sensitivity and spatial resolution have been validated experimentally using a laboratory x-ray source and the Argonne Advanced Photon Source. Possible applications include wide field-of-view or large x-ray aperture measurements in high-temperature plasmas, the state-of-the-art synchrotron, x-ray free electron laser, and pulsed power facilities.
