July 14, 2026 Software Open Access

Starke, Sebastian; Šmíd, Michal; Steinbach, Peter

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{
  "datePublished": "2026-07-14", 
  "creator": [
    {
      "name": "Starke, Sebastian", 
      "affiliation": "HZDR", 
      "@type": "Person"
    }, 
    {
      "name": "\u0160m\u00edd, Michal", 
      "affiliation": "HZDR", 
      "@type": "Person"
    }, 
    {
      "name": "Steinbach, Peter", 
      "affiliation": "HZDR", 
      "@type": "Person"
    }
  ], 
  "identifier": "https://doi.org/10.14278/rodare.4786", 
  "@context": "https://schema.org/", 
  "@id": "https://doi.org/10.14278/rodare.4786", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-43660"
  ], 
  "url": "https://rodare.hzdr.de/record/4786", 
  "@type": "SoftwareSourceCode", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "description": "<p>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.</p>", 
  "name": "Bremsstrahlung Denoising Software for X-ray Data Using Equivariant Neural Networks", 
  "version": "1.0"
}