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

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-FAA4-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-FAA9-3
Genre: Journal Article

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 Creators:
Ghosh, Sayak1, Author
Matty, Michael1, Author
Baumbach, Ryan1, Author
Bauer, Eric D.1, Author
Modic, K. A.2, Author              
Shekhter, Arkady1, Author
Mydosh, J. A.1, Author
Kim, Eun-Ah1, Author
Ramshaw, B. J.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Physics of Microstructured Quantum Matter, Max Planck Institute for Chemical Physics of Solids, Max Planck Society, ou_2466701              

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Free keywords: Machine learning, Silicon compounds, Spectroscopy, Ultrasonics, Basic characteristics, Competing orders, Correlated electron systems, Lower temperatures, Order parameter, Resonant Ultrasound Spectroscopy, Shear elastic modulus, Unconventional superconductivity, Ruthenium compounds, article, crystal, machine learning, spectroscopy, superconductivity, ultrasound
 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).

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Language(s): eng - English
 Dates: 2020-03-062020-03-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1126/sciadv.aaz4074
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Title: Science Advances
  Other : Sci. Adv.
Source Genre: Journal
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Publ. Info: Washington : AAAS
Pages: - Volume / Issue: 6 Sequence Number: eaaz4074 Start / End Page: 1 - 7 Identifier: ISSN: 2375-2548
CoNE: https://pure.mpg.de/cone/journals/resource/2375-2548