2,070 research outputs found
Electrically Driven Hyperbolic Nanophotonic Resonators as High Speed, Spectrally Selective Thermal Radiators
We introduce and experimentally demonstrate a new class of electrically
driven thermal emitter based on globally aligned carbon nanotube metamaterials
patterned as nanoscale ribbons. The metamaterial ribbons exhibit electronic and
photonic properties with extreme anisotropy, which enable low loss,
wavelength-compressed hyperbolic photonic modes along one axis and high
electrical resistivity and efficient Joule heating along the other axis.
Devices batch-fabricated on a single chip emit linearly polarized thermal
radiation with peak wavelengths dictated by their hyperbolic resonances, and
their low thermal mass yields infrared radiation modulation rates as high as
one megahertz. As a proof-of-concept demonstration, we show that two sets of
thermal emitters on a single chip, each operating with different spectral peak
positions and modulation rates, can be used to sense carbon dioxide with a
single detector. We anticipate that the combination of batch fabrication, wide
modulation bandwidth, and customized spectral tuning with hyperbolic chip-based
thermal emitters will enable new modalities in multiplexed infrared sources for
sensing, imaging, and metrology applications
Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow
Glitches represent a category of non-Gaussian and transient noise that
frequently intersects with gravitational wave (GW) signals, exerting a notable
impact on the processing of GW data. The inference of GW parameters, crucial
for GW astronomy research, is particularly susceptible to such interference. In
this study, we pioneer the utilization of temporal and time-spectral fusion
normalizing flow for likelihood-free inference of GW parameters, seamlessly
integrating the high temporal resolution of the time domain with the frequency
separation characteristics of both time and frequency domains. Remarkably, our
findings indicate that the accuracy of this inference method is comparable to
traditional non-glitch sampling techniques. Furthermore, our approach exhibits
greater efficiency, boasting processing times on the order of milliseconds. In
conclusion, the application of normalizing flow emerges as pivotal in handling
GW signals affected by transient noises, offering a promising avenue for
enhancing the field of GW astronomy research.Comment: 13 pages, 10 figure
Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Recent developments in deep learning techniques have offered an alternative
and complementary approach to traditional matched filtering methods for the
identification of gravitational wave (GW) signals. The rapid and accurate
identification of GW signals is crucial for the progress of GW physics and
multi-messenger astronomy, particularly in light of the upcoming fourth and
fifth observing runs of LIGO-Virgo-KAGRA. In this work, we use the 2D U-Net
algorithm to identify the time-frequency domain GW signals from stellar-mass
binary black hole (BBH) mergers. We simulate BBH mergers with component masses
from 5 to 80 and account for the LIGO detector noise. We find that
the GW events in the first and second observation runs could all be clearly and
rapidly identified. For the third observation run, about GW events could
be identified and GW190814 is inferred to be a BBH merger event. Moreover,
since the U-Net algorithm has advantages in image processing, the
time-frequency domain signals obtained through U-Net can preliminarily
determine the masses of GW sources, which could help provide the mass priors
for future parameter inferences. We conclude that the U-Net algorithm could
rapidly identify the time-frequency domain GW signals from BBH mergers and
provide great help for future parameter inferences.Comment: 11 pages, 9 figure
The Taiji-TianQin-LISA network: Precisely measuring the Hubble constant using both bright and dark sirens
In the coming decades, the space-based gravitational-wave (GW) detectors such
as Taiji, TianQin, and LISA are expected to form a network capable of detecting
millihertz GWs emitted by the mergers of massive black hole binaries (MBHBs).
In this work, we investigate the potential of GW standard sirens from the
Taiji-TianQin-LISA network in constraining cosmological parameters. For the
optimistic scenario in which electromagnetic (EM) counterparts can be detected,
we predict the number of detectable bright sirens based on three different MBHB
population models, i.e., pop III, Q3d, and Q3nod. Our results show that the
Taiji-TianQin-LISA network alone could achieve a constraint precision of
for the Hubble constant, meeting the standard of precision cosmology.
Moreover, the Taiji-TianQin-LISA network could effectively break the
cosmological parameter degeneracies generated by the CMB data, particularly in
the dynamical dark energy models. When combined with the CMB data, the joint
CMB+Taiji-TianQin-LISA data offer in the CDM model, which
is close to the latest constraint result obtained from the CMB+SN data. We also
consider a conservative scenario in which EM counterparts are not available.
Due to the precise sky localizations of MBHBs by the Taiji-TianQin-LISA
network, the constraint precision of the Hubble constant is expected to reach
. In conclusion, the GW standard sirens from the Taiji-TianQin-LISA
network will play a critical role in helping solve the Hubble tension and
shedding light on the nature of dark energy.Comment: 22 pages, 10 figures; published in Science China Physics Mechanics &
Astronom
The Balance of Th1/Th2 and LAP+Tregs/Th17 Cells Is Crucial for Graft Survival in Allogeneic Corneal Transplantation
Leaf nutrient traits of planted forests demonstrate a heightened sensitivity to environmental changes compared to natural forests
Leaf nutrient content (nitrogen, phosphorus) and their stoichiometric ratio (N/P) as key functional traits can reflect plant survival strategies and predict ecosystem productivity responses to environmental changes. Previous research on leaf nutrient traits has primarily focused on the species level with limited spatial scale, making it challenging to quantify the variability and influencing factors of forest leaf nutrient traits on a macro scale. This study, based on field surveys and literature collected from 2005 to 2020 on 384 planted forests and 541 natural forests in China, investigates the differences in leaf nutrient traits between forest types (planted forests, natural forests) and their driving factors. Results show that leaf nutrient traits (leaf nitrogen content (LN), leaf phosphorus content (LP), and leaf N/P ratio) of planted forests are significantly higher than those of natural forests (P< 0.05). The impact of climatic and soil factors on the variability of leaf nutrient traits in planted forests is greater than that in natural forests. With increasing forest age, natural forests significantly increase in leaf nitrogen and phosphorus content, with a significant decrease in N/P ratio (P< 0.05). Climatic factors are key environmental factors dominating the spatial variability of leaf nutrient traits. They not only directly affect leaf nutrient traits of planted and natural forest communities but also indirectly through regulation of soil nutrients and stand factors, with their direct effects being more significant than their indirect effects
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