Comments on the holographic description of Narain theories

We discuss the holographic description of Narain U(1)c× U(1)c conformal field theories, and their potential similarity to conventional weakly coupled gravitational theories in the bulk, in the sense that the effective IR bulk description includes “U(1) gravity” amended with additional light degrees of freedom. Starting from this picture, we formulate the hypothesis that in the large central charge limit the density of states of any Narain theory is bounded by below by the density of states of U(1) gravity. This immediately implies that the maximal value of the spectral gap for primary fields is ∆1 = c/(2πe). To test the self-consistency of this proposal, we study its implications using chiral lattice CFTs and CFTs based on quantum stabilizer codes. First we notice that the conjecture yields a new bound on quantum stabilizer codes, which is compatible with previously known bounds in the literature. We proceed to discuss the variance of the density of states, which for consistency must be vanishingly small in the large-c limit. We consider ensembles of code and chiral theories and show that in both cases the density variance is exponentially small in the central charge.

Magnetoclinicity: Density variance effects in large-scale instability in magnetohydrodynamic turbulence

<p>In the presence of strong compressibility an oblique configuration between the mean density gradient and magnetic field contributes to the electromotive force [1,2]. This effect can be called “magnetoclinicity” and may contribute to the formation of large-scale magnetic-field structure in compressible magnetohydrodynamic (MHD) turbulence. With the aid of the multiple-scale direct-interaction approximation (Multi-Scale DIA), a combination of the DIA and multiple-scale analysis, analytical expressions of the turbulent correlations (turbulent electromotive force, turbulent mass flux, turbulent heat flux, Reynolds stress, turbulent Maxwell  stress, etc.) are obtained for the compressible MHD turbulence. Utilizing these analytical results, a large-scale instability of the strongly compressible MHD turbulence is investigated. An analysis into normal modes of the periodic plane waves is performed to get a dispersion relation of the instability modes [3]. It is shown that, depending on the mean density configuration, the inhomogeneity of the mean density variation coupled with the density variance <ρ'<sup>2</sup>> (ρ': density fluctuation, <...>: average) leads to a finite growth of the mean magnetic disturbance at large scales. This magnetoclinicity effect counter-balance to the turbulent magnetic diffusivity, and contribute to the formation of large-scale magnetic fields. This magnetoclinicity effect is expected to play essential roles in global structure formation in strongly compressible plasma turbulence.</p><p>Reference</p><p>[1] N. Yokoi, “Electromotive force in strongly compressible magnetohydrodynamic turbulence,” J. Plasma Physics, <strong>84</strong>, 735840501, pp.1-26 (2018).</p><p>[2] N. Yokoi, “Mass and internal-energy transports in strongly compressible magnetohydrodynamic turbulence,” J. Plasma Physics, <strong>84</strong>, 775840603, pp.1-30 (2018).</p><p>[3] S. Chandrasekhar, Hydrodynamic and Hydromagnetic Stability (Oxford University Press, 1961).</p>

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.

Electromotive force in strongly compressible magnetohydrodynamic turbulence

Fully compressible magnetohydrodynamic (MHD) turbulence is investigated in the framework of the multiple-scale direct-interaction approximation. With the aid of the propagators (correlation and Green’s functions), fluctuating fields are solved, and turbulent correlations are estimated in highly compressible turbulence. We focus on the expression of the turbulent electromotive force (EMF). Obliqueness between the mean magnetic field and the mean-density gradient, the mean internal density gradient and the non-equilibrium mean velocity contributes to the EMF in the presence of the density variance, which is ubiquitous in turbulence in strongly variable density flows such as the shock-front region. This density-variance effect is expected to locally enhance the turbulence intensity across the shock front, leading to a fast reconnection.

The link between solenoidal turbulence and slow star formation in G0.253+0.016

Star formation in the Galactic disc is primarily controlled by gravity, turbulence, and magnetic fields. It is not clear that this also applies to star formation near the Galactic Centre. Here we determine the turbulence and star formation in the CMZ cloud G0.253+0.016. Using maps of 3 mm dust emission and HNCO intensity-weighted velocity obtained with ALMA, we measure the volume-density variance σρ /ρ 0=1.3±0.5 and turbulent Mach number $\mathcal{M}$ = 11±3. Combining these with turbulence simulations to constrain the plasma β = 0.34±0.35, we reconstruct the turbulence driving parameter b=0.22±0.12 in G0.253+0.016. This low value of b indicates solenoidal (divergence-free) driving of the turbulence in G0.253+0.016. By contrast, typical clouds in the Milky Way disc and spiral arms have a significant compressive (curl-free) driving component (b > 0.4). We speculate that shear causes the solenoidal driving in G0.253+0.016 and show that this may reduce the star formation rate by a factor of 7 compared to nearby clouds.