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International audience; Synchrotron X-ray µ-and nano-probes are increasingly affirming their relevance in cultural heritage applications, especially in material characterization of tiny and complex micro-sam-ples which are typical from archaeological and artistic artifacts. For such purposes, synchrotron radiation facilities are tailoring and optimizing beamlines and set-ups for CH, taking also ad-vantages from the challenges offered by the third-generation radiation sources. In ancient ceramics studies, relevant information for the identification of production centers and manufacture technology can be obtained in a non-invasive and non-destructive way at the micro-sample level by combining different SR based methods. However, the selection of appropriate beamlines, techniques and set-ups are critical for the success of the experiments. Fine and varnished wares (e.g., Attic and western-Greek colonial products) are an excellent case study for exploring challenges offered by synchrotron X-ray microprobes optimized to collect microchemical and phase-distribution maps. The determination of provenance and/or technological tracers is relevant in correctly classifying productions, often based only on ceramic paste, gloss macroscopic features or style. In addition, when these vessels are preserved in Museums as masterpieces or intact pieces the application of non-invasive approach at the micro sample is strictly required. Well-designed synchrotron µXRF and µXANES mapping experiments are able providing relevant clues for discriminating workshops and exploring technological aspects, which are fundamental in answering the current archaeological questions on varnished Greek or western-Greek colonial products.

The high resolution of synchrotron cryo-nano tomography can be easily undermined by setup instabilities and sample stage deficiencies such as runout or backlash. At the cost of limiting the sample visibility, especially in the case of bio-specimens, high contrast nano-beads are often added to the solution to provide a set of landmarks for a manual alignment. However, the spatial distribution of these reference points within the sample is difficult to control, resulting in many datasets without a sufficient amount of such critical features for tracking. Fast automatic methods based on tomography consistency are thus desirable, especially for biological samples, where regular, high contrast features can be scarce. Current off-the-shelf implementations of such classes of algorithms are slow if used on a real-world high-resolution dataset. In this paper, we present a fast implementation of a consistency-based alignment algorithm especially tailored to a multi-GPU system. Our implementation is released as open-source.