228 research outputs found
Magnetic relaxation and collective vortex creep in FeTeSe single crystal
We study the vortex dynamics in high-quality FeTeSe single
crystal by performing magnetization measurements of the screening current
density \emph{J} and flux creep rate \emph{S}. Temperature dependence of
\emph{S} shows a plateau in the intermediate temperature region with a high
creep rate 0.03, which is interpreted in the framework of the collective
creep theory. A crossover from elastic to plastic creep is observed. The glassy
exponent and barrier height for flux creep are directly determined by extended
Maley's method. \emph{J} with flux creep, obtained from magnetic hysteresis
loops, is successfully reproduced based on the collective creep analysis. We
also approach critical current density without flux creep by means of the
generalized inversion scheme, which proves that the \emph{l} and
\emph{T} pinning coexist in FeTeSe single crystal.Comment: 6 pages, 5 figure
Unabridged phase diagram for single-phased FeSexTe1-x thin films
A complete phase diagram and its corresponding physical properties are
essential prerequisites to understand the underlying mechanism of iron based
superconductivity. For the structurally simplest 11 (FeSeTe) system, earlier
attempts using bulk samples have not been able to do so due to the fabrication
difficulties. Here, thin FeSexTe1-x films with the Se content covering the full
range were fabricated by using pulsed laser deposition method. Crystal
structure analysis shows that all films retain the tetragonal structure in room
temperature. Significantly, the highest superconducting transition temperature
(TC = 20 K) occurs in the newly discovered domain, 0.6 - 0.8. The single-phased
superconducting dome for the full Se doping range is the first of its kind in
iron chalcogenide superconductors. Our results present a new avenue to explore
novel physics as well as to optimize superconductors
Observation of zero resistance above 100 K in PbCu(PO)O
Room-temperature superconductivity has always been regarded as the ultimate
goal in the fields of solid-state physics and materials science, with its
realization holding revolutionary significance, capable of triggering
significant changes in energy transmission and storage. However, achieving it
poses various challenges. Recent research revealed that material
PbCu(PO)O displays room-temperature superconductivity
under atmospheric pressure, sparking global interest in further exploration.
Here, we utilized solid-phase synthesis to obtain a polycrystalline sample of
PbCu(PO)O. X-ray diffraction confirmed its structural
consistency with referenced literature. Zero resistance, which is important
evidence for superconductivity, was observed above 100 K under ambient
pressure in our experiment. Our finding indicates that
PbCu(PO)O is a possible candidate for searching
high-temperature superconductors.Comment: 7 pages, 3 figure
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
Space-based gravitational wave detection is one of the most anticipated
gravitational wave (GW) detection projects in the next decade, which will
detect abundant compact binary systems. However, the precise prediction of
space GW waveforms remains unexplored. To solve the data processing difficulty
in the increasing waveform complexity caused by detectors' response and
second-generation time-delay interferometry (TDI 2.0), an interpretable
pre-trained large model named CBS-GPT (Compact Binary Systems Waveform
Generation with Generative Pre-trained Transformer) is proposed. For compact
binary system waveforms, three models were trained to predict the waveforms of
massive black hole binary (MBHB), extreme mass-ratio inspirals (EMRIs), and
galactic binary (GB), achieving prediction accuracies of 98%, 91%, and 99%,
respectively. The CBS-GPT model exhibits notable interpretability, with its
hidden parameters effectively capturing the intricate information of waveforms,
even with complex instrument response and a wide parameter range. Our research
demonstrates the potential of large pre-trained models in gravitational wave
data processing, opening up new opportunities for future tasks such as gap
completion, GW signal detection, and signal noise reduction
DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals
The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to
their complex waveforms, extended duration, and low signal-to-noise ratio
(SNR), making them more challenging to be identified compared to compact binary
coalescences. While matched filtering-based techniques are known for their
computational demands, existing deep learning-based methods primarily handle
time-domain data and are often constrained by data duration and SNR. In
addition, most existing work ignores time-delay interferometry (TDI) and
applies the long-wavelength approximation in detector response calculations,
thus limiting their ability to handle laser frequency noise. In this study, we
introduce DECODE, an end-to-end model focusing on EMRI signal detection by
sequence modeling in the frequency domain. Centered around a dilated causal
convolutional neural network, trained on synthetic data considering TDI-1.5
detector response, DECODE can efficiently process a year's worth of
multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year
data with accumulated SNR ranging from 50 to 120 and achieve a true positive
rate of 96.3% at a false positive rate of 1%, keeping an inference time of less
than 0.01 seconds. With the visualization of three showcased EMRI signals for
interpretability and generalization, DECODE exhibits strong potential for
future space-based gravitational wave data analyses.Comment: 13 pages, 5 figures, and 2 table
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