Bibliographic Details
| Title: |
Cadmium Zinc Telluride detectors for a next-generation hard X-ray telescope. |
| Authors: |
Tang, J.1 (AUTHOR), Kislat, F.2 (AUTHOR), Krawczynski, H.1 (AUTHOR) |
| Source: |
Astroparticle Physics. Mar2021, Vol. 128, pN.PAG-N.PAG. 1p. |
| Subjects: |
Cadmium zinc telluride, X-ray telescopes, Hard X-rays, Hybrid integrated circuits, Application-specific integrated circuits, Neutrino detectors |
| Abstract: |
We are currently developing Cadmium Zinc Telluride (CZT) detectors for a next-generation space-borne hard X-ray telescope which can follow up on the highly successful NuSTAR (Nuclear Spectroscopic Telescope Array) mission. Since the launch of NuSTAR in 2012, there have been major advances in the area of X-ray mirrors, and state-of-the-art X-ray mirrors can improve on NuSTAR's angular resolution of ∼ 1arcmin Half Power Diameter (HPD) to 15" or even 5" HPD. Consequently, the size of the detector pixels must be reduced to match this resolution. This paper presents detailed simulations of relatively thin (1mm thick) CZT detectors with hexagonal pixels at a next-neighbor distance of 150 μ m. The simulations account for the non-negligible spatial extent of the deposition of the energy of the incident photon, and include detailed modeling of the spreading of the free charge carriers as they move toward the detector electrodes. We discuss methods to reconstruct the energies of the incident photons, and the locations where the photons hit the detector. We show that the charge recorded in the brightest pixel and six adjacent pixels suffices to obtain excellent energy and spatial resolutions. The simulation results are being used to guide the design of a hybrid application-specific integrated circuit (ASIC)-CZT detector package. [ABSTRACT FROM AUTHOR] |
|
Copyright of Astroparticle Physics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |