JUNGFRAU detector for brighter x-ray sources: Solutions for IT and data science challenges in macromolecular crystallography

Filip Leonarski; Aldo Mozzanica; Martin Brückner; Carlos Lopez-Cuenca; Sophie Redford; Leonardo Sala; Andrej Babic; Heinrich Billich; Oliver Bunk; Bernd Schmitt; Meitian Wang

doi:10.1063/1.5143480

JUNGFRAU detector for brighter x-ray sources: Solutions for IT and data science challenges in macromolecular crystallography

Struct Dyn. 2020 Feb 26;7(1):014305. doi: 10.1063/1.5143480. eCollection 2020 Jan.

Affiliation

¹ Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland.

Abstract

In this paper, we present a data workflow developed to operate the adJUstiNg Gain detector FoR the Aramis User station (JUNGFRAU) adaptive gain charge integrating pixel-array detectors at macromolecular crystallography beamlines. We summarize current achievements for operating at 9 GB/s data-rate a JUNGFRAU with 4 Mpixel at 1.1 kHz frame-rate and preparations to operate at 46 GB/s data-rate a JUNGFRAU with 10 Mpixel at 2.2 kHz in the future. In this context, we highlight the challenges for computer architecture and how these challenges can be addressed with innovative hardware including IBM POWER9 servers and field-programmable gate arrays. We discuss also data science challenges, showing the effect of rounding and lossy compression schemes on the MX JUNGFRAU detector images.