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

被引:34
作者
Ghosh, Sayak [1 ]
Matty, Michael [1 ]
Baumbach, Ryan [2 ]
Bauer, Eric D. [3 ]
Modic, K. A. [4 ]
Shekhter, Arkady [2 ]
Mydosh, J. A. [5 ,6 ]
Kim, Eun-Ah [1 ]
Ramshaw, B. J. [1 ]
机构
[1] Cornell Univ, Lab Atom & Solid State Phys, Ithaca, NY 14853 USA
[2] Florida State Univ, Natl High Magnet Field Lab, Tallahassee, FL 32310 USA
[3] Los Alamos Natl Lab, Los Alamos, NM 87545 USA
[4] Max Planck Inst Chem Phys Solids, D-01187 Dresden, Germany
[5] Leiden Univ, Kamerlingh Onnes Lab, NL-2300 RA Leiden, Netherlands
[6] Leiden Univ, Inst Lorentz, NL-2300 RA Leiden, Netherlands
来源
SCIENCE ADVANCES | 2020年 / 6卷 / 10期
关键词
HIDDEN-ORDER; PHASE-TRANSITIONS; PROPAGATION; SPECIMENS; PRESSURE; BREAKING;
D O I
10.1126/sciadv.aaz4074
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The unusual correlated state that emerges in URu2Si2 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.
引用
收藏
页数:7
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