Super-resolution for x-ray applications with pixelated semiconductor tracking detectors using convolutional neural networks

Björn Eckert; Stefan Aschauer; Peter Holl; Petra Majewski; Thomas Zabel; Lothar Strüder

doi:10.1117/12.2576067

13 December 2020 Super-resolution for x-ray applications with pixelated semiconductor tracking detectors using convolutional neural networks

Björn Eckert, Stefan Aschauer, Peter Holl, Petra Majewski, Thomas Zabel, Lothar Strüder

Author Affiliations +

Proceedings Volume 11452, Software and Cyberinfrastructure for Astronomy VI; 114523O (2020) https://doi.org/10.1117/12.2576067
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

Abstract

In a semiconductor tracking detector, a single X-ray photon can create signals in a cluster of adjacent pixels. We present a novel technique to reconstruct the points of entry (PoEs) of X-ray photons from these clusters based on a convolutional neural network (CNN). The new method allows improving the spatial resolution into subpixel regime. Beside the improved accuracy of the reconstruction, the method is much less computational intensive than conventional event analyses and therefore can be run even on less powerful machines in realtime. Due to its special architecture, the CNN can handle different frame sizes without adjustments or retraining processes.

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Björn Eckert, Stefan Aschauer, Peter Holl, Petra Majewski, Thomas Zabel, and Lothar Strüder "Super-resolution for x-ray applications with pixelated semiconductor tracking detectors using convolutional neural networks", Proc. SPIE 11452, Software and Cyberinfrastructure for Astronomy VI, 114523O (13 December 2020); https://doi.org/10.1117/12.2576067

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