Coherent correlation imaging for resolving fluctuating states of matter

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 Schneider, Andi Barbour, Stuart B. Wilkins, Geoffrey S. D. Beach, Stefan Eisebitt and Bastian Pfau
Additional contact information
Christopher Klose: Max Born Institute
Felix Büttner: Department of Materials Science and Engineering, Massachusetts Institute of Technology
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
Bastian Pfau: Max Born Institute

Nature, 2023, vol. 614, issue 7947, 256-261

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-022-05537-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:614:y:2023:i:7947:d:10.1038_s41586-022-05537-9

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-022-05537-9

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().