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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7060057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
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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