Dark information of black hole radiation raised by dark energy

The "lost" information of black hole through the Hawking radiation was discovered being stored in the correlation among the non-thermally radiated particles [Phys. Rev. Lett 85, 5042 (2000), Phys. Lett. B 675, 1 (2009)]. This correlation information, which has not yet been proved locally observable in principle, is named by dark information. In this paper, we systematically study the influences of dark energy on black hole radiation, especially on the dark information. Calculating the radiation spectrum in the existence of dark energy by the approach of canonical typicality, which is reconfirmed by the quantum tunneling method, we find that the dark energy will effectively lower the Hawking temperature, and thus makes the black hole has longer life time. It is also discovered that the non-thermal effect of the black hole radiation is enhanced by dark energy so that the dark information of the radiation is increased. Our observation shows that, besides the mechanical effect (e.g., gravitational lensing effect), the dark energy rises the the stored dark information, which could be probed by a non-local coincidence measurement similar to the coincidence counting of the Hanbury-Brown -Twiss experiment in quantum optics.Comment: 21 pages, 3 figures, complete journal-info of Ref.[4] is added, comments are welcome ([email protected]

The Chemical Nature of Individual Size-Resolved Raindrops and Their Residual Particles Collected During High Atmospheric Loading for PM_2.5

Although it is well known that rain plays an important role in capturing air pollutants, its quantitative evaluation has not been done enough. In this study, the pollutant scavenging effect by size of raindrops was investigated by clarifying the chemical nature of individual size-resolved raindrops and their residual particles. Raindrops as a function of their size were collected using the raindrop collector devised by ourselves during high atmospheric loading for PM2.5. The raindrop number concentration (m−2 h−1) tended to drastically decrease as the drop size goes up. Particle scavenging rate, Rsca.(%), based on the actual measurement values were 38.7, 69.5, and 80.8% for the particles with 0.3–0.5, 0.5–1.0, and 1.0–2.0 μm diameter, respectively. S, Ca, Si, and Al ranked relatively high concentration in raindrops, especially small ones. Most of the element showed a continuous decrease in concentration with increasing raindrop diameter. The source profile by factor analysis for the components of residual particles indicated that the rainfall plays a valuable role in scavenging natural as well as artificial particles from the dirty atmosphere

Improving the Performance of R17 Type-II Codebook with Deep Learning

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.Comment: Accepted by IEEE GLOBECOM 2023, conference version of Arxiv:2305.0808