Author
Listed:
- Christopher Klose
(Max Born Institute)
- Felix Büttner
(Department of Materials Science and Engineering, Massachusetts Institute of Technology
Brookhaven National Laboratory
Helmholtz-Zentrum für Materialien und Energie)
- Wen Hu
(Brookhaven National Laboratory)
- Claudio Mazzoli
(Brookhaven National Laboratory)
- Kai Litzius
(Department of Materials Science and Engineering, Massachusetts Institute of Technology)
- Riccardo Battistelli
(Helmholtz-Zentrum für Materialien und Energie)
- Sergey Zayko
(University of Göttingen)
- Ivan Lemesh
(Department of Materials Science and Engineering, Massachusetts Institute of Technology)
- Jason M. Bartell
(Department of Materials Science and Engineering, Massachusetts Institute of Technology)
- Mantao Huang
(Department of Materials Science and Engineering, Massachusetts Institute of Technology)
- Christian M. Günther
(Technische Universität Berlin)
- Michael Schneider
(Max Born Institute)
- Andi Barbour
(Brookhaven National Laboratory)
- Stuart B. Wilkins
(Brookhaven National Laboratory)
- Geoffrey S. D. Beach
(Department of Materials Science and Engineering, Massachusetts Institute of Technology)
- Stefan Eisebitt
(Max Born Institute
Technische Universität Berlin)
- Bastian Pfau
(Max Born Institute)
Abstract
Fluctuations and stochastic transitions are ubiquitous in nanometre-scale systems, especially in the presence of disorder. However, their direct observation has so far been impeded by a seemingly fundamental, signal-limited compromise between spatial and temporal resolution. Here we develop coherent correlation imaging (CCI) to overcome this dilemma. Our method begins by classifying recorded camera frames in Fourier space. Contrast and spatial resolution emerge by averaging selectively over same-state frames. Temporal resolution down to the acquisition time of a single frame arises independently from an exceptionally low misclassification rate, which we achieve by combining a correlation-based similarity metric1,2 with a modified, iterative hierarchical clustering algorithm3,4. We apply CCI to study previously inaccessible magnetic fluctuations in a highly degenerate magnetic stripe domain state with nanometre-scale resolution. We uncover an intricate network of transitions between more than 30 discrete states. Our spatiotemporal data enable us to reconstruct the pinning energy landscape and to thereby explain the dynamics observed on a microscopic level. CCI massively expands the potential of emerging high-coherence X-ray sources and paves the way for addressing large fundamental questions such as the contribution of pinning5–8 and topology9–12 in phase transitions and the role of spin and charge order fluctuations in high-temperature superconductivity13,14.
Suggested Citation
Christopher Klose & Felix Büttner & Wen Hu & Claudio Mazzoli & Kai Litzius & Riccardo Battistelli & Sergey Zayko & Ivan Lemesh & Jason M. Bartell & Mantao Huang & Christian M. Günther & Michael Schnei, 2023.
"Coherent correlation imaging for resolving fluctuating states of matter,"
Nature, Nature, vol. 614(7947), pages 256-261, February.
Handle:
RePEc:nat:nature:v:614:y:2023:i:7947:d:10.1038_s41586-022-05537-9
DOI: 10.1038/s41586-022-05537-9
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