July 14, 2026 Software Open Access

Starke, Sebastian; Šmíd, Michal; Steinbach, Peter

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  <dc:creator>Starke, Sebastian</dc:creator>
  <dc:creator>Šmíd, Michal</dc:creator>
  <dc:creator>Steinbach, Peter</dc:creator>
  <dc:date>2026-07-14</dc:date>
  <dc:description>Large ammount of energetic bremstrahlung is created in experiments where ultra high intensity laser interacts with solid targets. This constitutes a distinct background signal on detectors used in the experiment. Such background then can obscure the desired measured signal. We provide a tool which can distinguish this background from the useful signal. The first and prominent case where this was utilized is in the detection of Small angle x-ray scattering (SAXS) diagnostics at the HED instrument at European XFEL, but we believe this tool could find much broader usage.</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/4786</dc:identifier>
  <dc:identifier>10.14278/rodare.4786</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:4786</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-43660</dc:relation>
  <dc:relation>doi:10.14278/rodare.4785</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Bremsstrahlung Denoising Software for X-ray Data Using Equivariant Neural Networks</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
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