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search.filters.applied.f.access: openAccess × End date: 2023 × Start date: 2020 × DDC: 004 × Version: publishedVersion × WGL Institute: MBI ×

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    NTMpy: An open source package for solving coupled parabolic differential equations in the framework of the three-temperature model
    (Amsterdam : North Holland Publ. Co., 2021) Alber, Lukas; Scalera, Valentino; Unikandanunni, Vivek; Schick, Daniel; Stefano Bonetti
    The NTMpy code package allows for simulating the one-dimensional thermal response of multilayer samples after optical excitation, as in a typical pump-probe experiment. Several Python routines are combined and optimized to solve coupled heat diffusion equations in one dimension, on arbitrary piecewise homogeneous material stacks, in the framework of the so-called three-temperature model. The energy source deposited in the material is modelled as a light pulse of arbitrary cross-section and temporal profile. A transfer matrix method enables the calculation of realistic light absorption in presence of scattering interfaces as in multilayer samples. The open source code is fully object-oriented to enable a user-friendly and intuitive interface for adjusting the physically relevant input parameters. Here, we describe the mathematical background of the code, we lay out the workflow, and we validate the functionality of our package by comparing it to commercial software, as well as to experimental transient reflectivity data recorded in a pump-probe experiment with femtosecond light pulses.
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    Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning
    (London : Nature Publ. Group, 2023) Zimmermann, Julian; Beguet, Fabien; Guthruf, Daniel; Langbehn, Bruno; Rupp, Daniela
    Single-shot coherent diffraction imaging of isolated nanosized particles has seen remarkable success in recent years, yielding in-situ measurements with ultra-high spatial and temporal resolution. The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images, which are needed for the structure determination of specimens with greater structural variety and dynamic experiments. The size of the datasets, however, represents a monumental problem for their analysis. Here, we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling. By introducing the concept of projection learning, we extend self-supervised contrastive learning to the context of coherent diffraction imaging and achieve a dimensionality reduction producing semantically meaningful embeddings that align with physical intuition. The method yields substantial improvements compared to previous approaches, paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers.