Second Repeating FRB 180814.J0422+73: Ten-year Fermi-LAT Upper Limits and Implications

The second repeating fast radio burst source, FRB 180814.J0422+73, was detected recently by the CHIME collaboration. We use the ten-year Fermi Large Area Telescope archival data to place a flux upper limit in the energy range of 100 MeV−10 GeV at the position of the source, which is ~1.1 × 10−11 erg cm−2 s−1 for a six-month time bin on average, and ~2.4 × 10−12 erg cm−2 s−1 for the entire ten-year time span. For the maximum redshift of z = 0.11, the ten-year upper limit of luminosity is ~7.3 × 1043 erg s−1. We utilize these upper limits to constrain the fast radio burst (FRB) progenitor and central engine. For the rotation-powered young magnetar model, the upper limits can pose constraints on the allowed parameter space for the initial rotational period and surface magnetic field of the magnetar. We also place significant constraints on the kinetic energy of a relativistic external shock wave, ruling out the possibility that there existed a gamma-ray burst (GRB) beaming toward Earth during the past ten years as the progenitor of the repeater. The case of an off-beam GRB is also constrained if the viewing angle is not much greater than the jet opening angle. All of these constraints are more stringent if FRB 180814.J0422+73 is at a closer distance

Brane worlds in gravity with auxiliary fields

Recently, Pani, Sotiriou, and Vernieri explored a new theory of gravity by adding nondynamical fields, i.e., gravity with auxiliary fields [Phys. Rev. D 88, 121502(R) (2013)]. In this gravity theory, higher-order derivatives of matter fields generically appear in the field equations. In this paper we extend this theory to any dimensions and discuss the thick braneworld model in five dimensions. Domain wall solutions are obtained numerically. The stability of the brane system under the tensor perturbation is analyzed. We find that the system is stable under the tensor perturbation and the gravity zero mode is localized on the brane. Therefore, the four-dimensional Newtonian potential can be realized on the brane.Comment: 7 pages, 4 figure

Resummation of Boson-Jet Correlation at Hadron Colliders

We perform a precise calculation of the transverse momentum ($\vec{q}_T$) distribution of the boson+jet system in boson production events. The boson can be either a photon, $W$, $Z$ or Higgs boson with mass $m_V$, and $\vec{q}_T$ is the sum of the transverse momenta of the boson and the leading jet with magnitude $q_T=|\vec q_T|$. Using renormalization group techniques and soft-collinear effective theory, we resum logarithms $\log(Q/q_T)$ and $\log R$ at next-to-leading logarithmic accuracy including the non-global logarithms, where $Q$ and $R$ are respectively the hard scattering energy and the radius of the jet. Specifically, we investigate two scenarios of $p^J_T \lesssim m_V$ or $p^J_T \gtrsim m_V$ in $Z$+jet events, and we examine the $q_T$ distributions with different jet radii and study the effect of non-global logarithms. In the end we compare our theoretical calculations with Monte Carlo simulations and data from the LHC.Comment: 35 pages, 7 figure

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. De-convolutional layers are then used to recover the image details. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. First, The skip connections allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to de-convolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than all previously reported state-of-the-art methods.Comment: Accepted to Proc. Advances in Neural Information Processing Systems (NIPS'16). Content of the final version may be slightly different. Extended version is available at http://arxiv.org/abs/1606.0892