Enhanced micrometre spatial resolution and usable photon flux for hybrid pixel detectors via deep-learning-based X-ray localization
Year: 2026
Type: preprint
Abstract: Micrometre spatial resolution is achieved for X-rays with the MÖNCH detector, a charge-integrating hybrid pixel detector with a 25 μm pixel pitch, by exploiting charge sharing between neighbouring pixels. The conventional analytical interpolation method, however, relies on a global mapping derived from the charge-sharing statistics and is applicable only to isolated single-photon clusters, which limits both the achievable resolution and the usable photon flux. To overcome these limitations, we trained deep learning models to localize single-photon and pile-up events based on high-fidelity simulation data. For knife-edge measurements at 12 keV and a low occupancy of 0.74% (photons per pixel and frame), where single-photon events dominate, the proposed method reaches a of the edge spread function of 1.36 μm, compared with 1.54 μm for the conventional eta interpolation. We demonstrate experimentally that resolving up to three photons per cluster raises the reconstruction efficiency from 59% to 93% at a 2.4{\times} higher occupancy of 1.81%, while preserving a of 1.39 μm.
Source: arXiv (Cornell University)
Authors: Xie, Xiangyu, Bergamaschi, Anna, Brückner, Martin, Carulla, Maria, Celko, Tomáš, Dinapoli, Roberto, Ebner, Simon, Emiliani, Simone, Ferjaoui, Khalil Daniel, Fröjdh, Erik Lars, Gautam, Viveka, Greiffenberg, Dominic, Hasanaj, Shqipe, Hinger, Viktoria, Hürst, Marius, Kedych, Vadym, King, Thomas, Li, Shuqi, Lopez-Cuenca, Carlos, Lunin, Leonid, Marone, Federica, Mazzoleni, Alice Francesca, Mezza, Davide, Moses, Scharon, Moustakas, Konstantinos, Mozzanica, Aldo, Mulvey, Jonathan Franklin, Müller, Martin, Ruder, Christian, Schmitt, Bernd, Silletta, Saverio, Thattil, Dhanya, Zhang, Jiaguo
Institution: Paul Scherrer Institute
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: VINNOVA, Svenska Forskningsrådet Formas
OpenAlex ID: W7221120012