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DTSTART;TZID=Europe/Berlin:20220608T160000
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DTSTAMP:20260504T173216
CREATED:20250130T114954Z
LAST-MODIFIED:20250402T075558Z
UID:2763-1654704000-1654707600@laserlab-europe.eu
SUMMARY:Laserlab-Europe Talk: AI for X-ray scattering
DESCRIPTION:Speaker: Nico Hoffmann (HZDR\, Germany)  \n			\n				Watch the Talk\n			\n				\n				\n				\n				\n				The long term and sustainable success of the X-ray community essentially depends on its ability to meet growing challenges in handling and analyzing data of increasing volume and complexity. \nMachine Learning (ML) provide a smart solution enabling dramatically increasing the output of X-ray scattering facilities regarding acceleration of the data analysis\, optimization of beam time usage and\, consequently\, growth of publication rate. Analysis of scattering data is a very time-consuming process as it requires solving an ill-posed inverse problem to infer properties of the imaged object. \nWe will be discussing two state-of-the-art methods to solve this task: 1) ML-based estimation of the most important parameters of the object in a single step given the experimentally acquired scattering image; 2) Iterative ML-assisted phasing based on automatic differentiation that can be very easily used for fast reconstruction of multiple X-ray scattering modalities such as CDI\, mono- & polychromatic Ptychography as well as Holography. \nSome parameters exhibit low contribution to the acquired data which also hampers reliable predictions and discrimination of these parameters. We show that normalising flows can be used to recover the predictive posterior distribution of these parameters to resolve ambiguous situations and provide information about the reliability of the estimate.
URL:https://laserlab-europe.eu/event/laserlab-europe-talk-ai-for-x-ray-scattering/
LOCATION:Online
CATEGORIES:laserlab-europe events,laserlab-europe talk
ATTACH;FMTTYPE=image/jpeg:https://laserlab-europe.eu/wp-content/uploads/2024/11/lle-talk_2022-06_hoffmann_x-ray-scattering.jpg
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