Title of Dataset: Dataset for Revealing isotropic abundant low-energy excitations in UTe2 through complex microwave surface impedance

Date of Issue: February 11, 2025

Related Publication:
https://doi.org/10.48550/arXiv.2502.07955

Author Information:

Corresponding Author:
Arthur Carlton-Jones, acarlton@umd.edu

Principle Investigator:
Steven Anlage, anlage@umd.edu

Funding Acknowledgements:
The work of AC-J and SMA was supported by NSF-DMR/2004386, and ARO/FSDL under grant W911NF-24-1-0153.  The work of AS, YSE, IMH, SRS, JP and NPB was supported by the U.S. Department of Energy Award No. DE-SC-0019154 (sample characterization), the Air Force Office of Scientific Research under Grant No. FA9950-22-1-0023 (materials synthesis), and the Gordon and Betty Moore Foundation’s EPiQS Initiative through Grant No. GBMF9071, the National Institute of Standards and Technology, and the Maryland Quantum Materials Center. SRS acknowledges support from the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301.

Abstract:
This is the dataset used to perform all the analysis and create all the figures of the paper: Revealing isotropic abundant low-energy excitations in UTe2 through complex microwave surface impedance.

Methodological Information:
All experimental transmission data (S_21) in Revealing isotropic abundant low-energy excitations in UTe2 through complex microwave surface impedance was collected using a Keysight vector network analyzer (VNA). The transmission parameter was collected for several modes and temperatures. The raw S_21 data was processed into resonant frequency and quality factor using a Lorentzian fit to S_21 vs frequency for each mode and temperature. 

Once the .mat data files are downloaded, the data can be viewed natively using MATLAB, but Python, Mathematica, etc. can also process and analyze .mat files.

File List:
UTe2_B39_transmission_and_resonance_fittings.mat contains data for sample B39 sample run and its corresponding background run. It also contains resonance fittings to both of these runs, interpolation of the background data, and subtraction of the background data. UTe2_B40_transmission_and_resonance_fittings.mat contains the corresponding data for sample B40.

Variables: (All frequencies are in GHz, all temperatures are in K, and all phases are in degrees wrapped at +/- 180.)
algorithm : string : the algorithm used to determine the resonant frequency and quality factor
delta_f0 : (N_modes x N_T) : change in resonant frequency from the minimum temperature of the sample run ( T(1) ) due to sample alone ( = delta_f0_tot - delta_f0_r_spline )
delta_f0_r : (N_modes_r x N_T_r) : change in f0_r from the minimum temperature of the background run ( T_r(1) ) ( = f0_r - f0_r_min )
delta_f0_r_spline : (N_modes_r x N_T) : change in f0_r_spline from the minimum temperature of the sample run ( T(1) ) ( = f0_r_spline - f0_r_spline_min)
delta_f0_tot : (N_modes x N_T) : change in f0 from the minimum temperature of the sample run ( T(1) ) ( f0_tot - f0_tot_min )
df_r : (N_modes_r x N_T_r) : 3dB bandwidth for the background run ( = f0_r/Q_r )
df_tot : (N_modes x N_T) : 3dB bandwidth for the sample run ( = f0_tot/Q_tot )
f0_r : (N_modes_r x N_T_r) : resonant frequency for the background run
f0_r_min : (N_modes_r x N_T_r) : resonant frequency for the background run at the minimum temperature of the background run ( T_r(1) ) (the temperature axis is preserved, but the data is constant along it) ( = f0_r(:,1)*ones(1,N_T_r) )
f0_r_spline : (N_modes_r x N_T) : resonant frequency for the background run interpolated onto the temperature grid of the sample run
f0_r_spline_min : (N_modes_r x N_T) : resonant frequency for the background run interpolated to the minimum temperature of the sample run ( T(1) ) (the temperature axis is preserved, but the data is constant along it) ( = f0_r_spline(:,1)*ones(1,N_T) )
f0_tot : (N_modes x N_T) : resonant frequency for the sample run
f0_tot_min : (N_modes x N_T) : resonant frequency for the sample run at the minimum temperature of the sample run ( T(1) ) (the temperature axis is preserved, but the data is constant along it) ( = f0_tot(:,1)*ones(1,N_T) )
f_T : (N_modes x N_T_full x N_f) : frequency grid for transmission measurement for the sample run
f_T_r : (N_modes_r x N_T_r x N_f_r) : frequency grid for transmission measurement for the background run
interp_method : string : the algorithm used to interpolate resonance fitting parameters for the background run
modes_skip : (1 x ~ ) : indices of modes which were unable to be analyzed (i.e. due to insufficient coupling to being able to do the background subtraction)
N_f : int : number of frequency points in transmission measurement for the sample run
N_f_r : int : number of frequency points in transmission measurement for the background run
N_modes : int : number of modes for the sample run
N_modes_r : int : number of modes for the background run (must be the same as N_modes: the modes must actually correspond between sample and background runs)
N_T : int : number of temperatures for the sample run after averaging among equivalent temperature (measurements are repeated for some temperatures)
N_T_full : int : original number of temperatures for the sample run (before averaging)
N_T_r : int : number of temperatures for the background run
phase21_T : (N_modes x N_T_full x N_f) :  transmission phase data for the sample run
phase21_T_r : (N_modes_r x N_T_r x N_f_r) :  transmission phase data for the background run
Q : (N_modes x N_T) : background subtracted quality factor due to sample alone ( = 1/(1/Q_tot - 1/Q_r_spline) )
Q_r : (N_modes_r x N_T_r) : quality factor for the background run
Q_r_spline : (N_modes_r x N_T) : quality factor for the background run interpolated onto the temperature grid of the sample run
Q_tot : (N_modes_r x N_T_r) : quality factor for the sample run
res_fit_result : (N_modes x N_T_full x 6) : resonance fitting parameters for the sample run [ Let a = squeeze(res_fit_result(i_modes,i_T,:)), and f = squeeze(f_T(i_modes,i_T,:)). Theory curve for S_21  = (a(1)+a(2)*(f-a(4))+(a(3)+a(6)*(f-a(4)))./sqrt(1+4*((f-a(4))/a(5)).^2)). Most importantly, before averaging, f0_tot(i_modes,i_T) = a(4), and df_tot(i_modes,i_T) = a(5). ]
res_fit_result_r : (N_modes_r x N_T_r x 6) : resonance fitting parameters for the sample run [ Let a = squeeze(res_fit_result(i_modes,i_T,:)), and f = squeeze(f_T_r(i_modes,i_T,:)). Theory curve for S_21  = (a(1)+a(2)*(f-a(4))+(a(3)+a(6)*(f-a(4)))./sqrt(1+4*((f-a(4))/a(5)).^2)). Most importantly, f0_r(i_modes,i_T) = a(4), and df_r(i_modes,i_T) = a(5). ]
s21_T : (N_modes x N_T_full x N_f) :  transmission magnitude (dB) data for the sample run [ Data is in log scale. S_21 in linear scale = 10.^(s21_T/20). ]
s21_T_r : (N_modes_r x N_T_r x N_f_r) :  transmission magnitude (dB) data for the background run [ Data is in log scale. S_21 in linear scale = 10.^(s21_T_r/20). ]
sample : string : sample and measurement identifier
sig_<...> : uncertainty for <...> [ It can be either a measurement, fitting, or propagation uncertainty. ]
T : (N_T x 1) : measured temperature steps for the sample run after averaging among equivalent temperature (measurements are repeated for some temperatures)
T_full : (N_T_full x 1) : original temperature steps for the sample run (before averaging)
T_r : (N_T_r x 1) : measured temperature steps for the background run