arXiv subjects

M. Abedi

Publications and source records attributed to M. Abedi.

4 recordsLinked to original sources

arXiv2023

Gap structure of the non-symmorphic superconductor LaNiGa2 probed by muSR

We report muon spin rotation (muSR) measurements of the temperature dependence of the absolute value of the magnetic penetration depth and the magnetic field dependence of the vortex core size in the mixed state of the non-symmorphic superconductor LaNiGa2. The temperature dependence of the normalized superfluid density is shown to be well described by a two-band model with strong interband coupling. Consistent with a strong coupling of the superconducting condensates in two different bands, we show that the field dependence of the vortex core size resembles that of a single-band superconductor. Our results lend support to the proposal that LaNiGa2 is a fully-gapped, internally antisymmetric nonunitary spin-triplet superconductor.

cond-mat.supr-con↗

arXiv2023

$\mu^+$ Knight Shift in UTe$_2$: Evidence for Relocalization in a Kondo Lattice

The local magnetic susceptibility of the spin-triplet superconductor UTe$_2$ has been investigated by positive muon ($\mu^+$) Knight shift measurements in the normal state. Three distinct $\mu^+$ Knight shift components are observed for a magnetic field applied parallel to the $c$ axis. Two of these exhibit a breakdown in the linear relationship with the bulk magnetic susceptibility ($\chi$) below a temperature $T^* \! \sim \! 30$ K, which points to a gradual emergence of a correlated Kondo liquid. Below $T_{\rm r} \! \sim \! 12$ K linearity is gradually restored, indicating partial relocalization of the Kondo liquid quasiparticles. The third Knight shift component is two orders of magnitude larger, and despite the $c$-axis alignment of the external field, scales with the $a$-axis $\chi$ above $T_{\rm r} \! \sim \! 12$ K. We conjecture that this component is associated with magnetic clusters and the change in the temperature dependence of all three Knight shift components below $T_{\rm r}$ is associated with a change in magnetic correlations. Our findings indicate that prior to the onset of superconductivity the development of the itinerant heavy-electron fluid is halted by a gradual development of local U $5f$-moment fluctuations.

cond-mat.str-el↗

arXiv2023

Ubiquitous Spin Freezing in the Superconducting State of UTe2

In most superconductors electrons form Cooper pairs in a spin-singlet state mediated by either phonons or by long-range interactions such as spin fluctuations. The superconductor UTe$_2$ is a rare material wherein electrons are believed to form pairs in a unique spin-triplet state with potential topological properties. While spin-triplet pairing may be mediated by ferromagnetic or antiferromagnetic fluctuations, experimentally, the magnetic properties of UTe$_2$ are unclear. By way of muon spin rotation/relaxation ($μ$SR) measurements on independently grown UTe$_2$ single crystals we demonstrate the existence of magnetic clusters that gradually freeze into a disordered spin frozen state at low temperatures. Our findings suggest that inhomogeneous freezing of magnetic clusters is linked to the ubiquitous residual linear term in the temperature dependence of the specific heat ($C$) and the low-temperature upturn in $C/T$ versus $T$. The omnipresent magnetic inhomogeneity has potential implications for experiments aimed at establishing the intrinsic low-temperature properties of UTe$_2$.

cond-mat.supr-con↗

arXiv2012

Automatic ECG Beat Arrhythmia Detection

Background: In recent years automated data analysis techniques have drawn great attention and are used in almost every field of research including biomedical. Artificial Neural Networks (ANNs) are one of the Computer- Aided- Diagnosis tools which are used extensively by advances in computer hardware technology. The application of these techniques for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. Methods: In this study we are using Probabilistic Neural Networks (PNN) as an automatic technique for ECG signal analysis along with a Genetic Algorithm (GA). As every real signal recorded by the equipment can have different artifacts, we need to do some preprocessing steps before feeding it to the ANN. Wavelet transform is used for extracting the morphological parameters and median filter for data reduction of the ECG signal. The subset of morphological parameters are chosen and optimized using GA. We had two approaches in our investigation, the first one uses the whole signal with 289 normalized and de-noised data points as input to the ANN. In the second approach after applying all the preprocessing steps the signal is reduced to 29 data points and also their important parameters extracted to form the ANN input with 35 data points. Results: The outcome of the two approaches for 8 types of arrhythmia shows that the second approach is superior than the first one with an average accuracy of %99.42.

cs.NE↗