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One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning

The unusual correlated state that emerges in URu(2)Si(2) below T(HO) = 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 s...

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Autores principales: Ghosh, Sayak, Matty, Michael, Baumbach, Ryan, Bauer, Eric D., Modic, K. A., Shekhter, Arkady, Mydosh, J. A., Kim, Eun-Ah, Ramshaw, B. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060057/
https://www.ncbi.nlm.nih.gov/pubmed/32181367
http://dx.doi.org/10.1126/sciadv.aaz4074
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author Ghosh, Sayak
Matty, Michael
Baumbach, Ryan
Bauer, Eric D.
Modic, K. A.
Shekhter, Arkady
Mydosh, J. A.
Kim, Eun-Ah
Ramshaw, B. J.
author_facet Ghosh, Sayak
Matty, Michael
Baumbach, Ryan
Bauer, Eric D.
Modic, K. A.
Shekhter, Arkady
Mydosh, J. A.
Kim, Eun-Ah
Ramshaw, B. J.
author_sort Ghosh, Sayak
collection PubMed
description The unusual correlated state that emerges in URu(2)Si(2) below T(HO) = 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 T(HO). 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.
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spelling pubmed-70600572020-03-16 One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning Ghosh, Sayak Matty, Michael Baumbach, Ryan Bauer, Eric D. Modic, K. A. Shekhter, Arkady Mydosh, J. A. Kim, Eun-Ah Ramshaw, B. J. Sci Adv Research Articles The unusual correlated state that emerges in URu(2)Si(2) below T(HO) = 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 T(HO). 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. American Association for the Advancement of Science 2020-03-06 /pmc/articles/PMC7060057/ /pubmed/32181367 http://dx.doi.org/10.1126/sciadv.aaz4074 Text en Copyright © 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). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Ghosh, Sayak
Matty, Michael
Baumbach, Ryan
Bauer, Eric D.
Modic, K. A.
Shekhter, Arkady
Mydosh, J. A.
Kim, Eun-Ah
Ramshaw, B. J.
One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning
title One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning
title_full One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning
title_fullStr One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning
title_full_unstemmed One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning
title_short One-component order parameter in URu(2)Si(2) uncovered by resonant ultrasound spectroscopy and machine learning
title_sort one-component order parameter in uru(2)si(2) uncovered by resonant ultrasound spectroscopy and machine learning
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060057/
https://www.ncbi.nlm.nih.gov/pubmed/32181367
http://dx.doi.org/10.1126/sciadv.aaz4074
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