English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

One-component order parameter in URu2Si2 uncovered by resonant ultrasound spectroscopy and machine learning

MPS-Authors
/persons/resource/persons198795

Modic,  K. A.
Physics of Microstructured Quantum Matter, Max Planck Institute for Chemical Physics of Solids, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Ghosh, S., Matty, M., Baumbach, R., Bauer, E. D., Modic, K. A., Shekhter, A., et al. (2020). One-component order parameter in URu2Si2 uncovered by resonant ultrasound spectroscopy and machine learning. Science Advances, 6: eaaz4074, pp. 1-7. doi:10.1126/sciadv.aaz4074.


Cite as: http://hdl.handle.net/21.11116/0000-0005-FAA4-8
Abstract
The unusual correlated state that emerges in URu2Si2 below THO = 17.5 K is known as “hidden order” because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are “hidden.” We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across THO. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems. © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).