13,175 research outputs found

    Ultra-low-frequency electromagnetic waves as signals and special counterparts of gravitational waves (from binary mergers) having tensorial and possible nontensorial polarizations

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    Gravitational waves (GWs, from binary merger) interacting with super-strong magnetic fields of the neutron star (in the same binary system), would lead to perturbed electromagnetic waves [EMWs, in the same frequencies of these GWs, partially in the ultra-low-frequency (ULF) band for the EMWs]. Such perturbed ULF-EMWs are not only the signals, but also a new type of special EM counterparts of the GWs. Here, generation of the perturbed ULF-EMWs is investigated for the first time, and the strengths of their magnetic components are estimated to be around 10^{-12}Tesla to 10^{-17}Tesla (in fISCO) at the Earth for various cases [not including the influence of interstellar medium (ISM)].The components with higher frequencies of the ULF-EMWs (e.g., especially produced by the GWs of the post-merger stage) above 1.8kHz (typical plasma frequency around solar system in the Milky way), could propagate through the ISM from the source until the Earth, and the perturbed ULF-EMWs will be reprocessed before they arrived at the Earth due to the ISM. Also, the waveforms of the perturbed ULF-EMWs will be modified into shapes different but related to the waveforms of the GWs, by the amplification process during the binary mergers which could amplify the magnetic fields into 10^{12}Tesla or even higher. Specific connection relationships between the polarizations of the perturbed ULF-EMWs and the polarizations (tensorial and possible nontensorial) of the GWs of binary mergers, are also addressed here. Characteristic properties of the perturbed ULF-EMWs (which would bring us some different new information of fundamental properties of the gravity and Universe) will be very helpful for extracting the signals from background noise for possible observations in the future.Comment: 16 pages, 6 figure

    Counting Multiplicities in a Hypersurface over a Number Field

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    We fix a counting function of multiplicities of algebraic points in a projective hypersurface over a number field, and take the sum over all algebraic points of bounded height and fixed degree. An upper bound for the sum with respect to this counting function will be given in terms of the degree of the hypersurface, the dimension of the singular locus, the upper bounds of height, and the degree of the field of definition.Comment: 23 page

    A twisted βˆ‚Λ‰f\bar{\partial}_f-Neumann problem and Toeplitz nn-tuples from singularity theory

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    A twisted βˆ‚Λ‰f\bar{\partial}_f-Neumann problem associated to a singularity (On,f)(\mathscr{O}_n,f) is established. By constructing the connection to the Koszul complex for toeplitz nn-tuples (f1,⋯ ,fn)(f_1,\cdots,f_n) on Bergman spaces B0(D)B^0(D), we can solve this βˆ‚Λ‰f\bar{\partial}_f-Neumann problem. Moreover, we can compute the cohomology of the L2L^2 holomorphic Koszul complex (Bβˆ—(D),βˆ‚f∧)(B^*(D),\partial f\wedge) explicitlyComment: 20 page

    Stability of Steady Solutions to Reaction-Hyperbolic Systems for Axonal Transport

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    This paper is concerned with the stability of steady solutions to initial-boundary-value problems of reaction-hyperbolic systems for axonal transport. Under proper structural assumptions, we clarify the relaxation structure of the reaction-hyperbolic systems and show the time-asymptotic stability of steady solutions or relaxation boundary-layers

    Improving spin-based noise sensing by adaptive measurements

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    Localized spins in the solid state are attracting widespread attention as highly sensitive quantum sensors with nanoscale spatial resolution and fascinating applications. Recently, adaptive measurements were used to improve the dynamic range for spin-based sensing of deterministic Hamiltonian parameters. Here we explore a very different direction -- spin-based adaptive sensing of random noises. First, we identify distinguishing features for the sensing of magnetic noises compared with the estimation of deterministic magnetic fields, such as the different dependences on the spin decoherence, the different optimal measurement schemes, the absence of the modulo-2\pi phase ambiguity, and the crucial role of adaptive measurement. Second, we perform numerical simulations that demonstrate significant speed up of the characterization of the spin decoherence time via adaptive measurements. This paves the way towards adaptive noise sensing and coherence protection.Comment: 13 pages, 7 figure

    Simulating the Chiral Magnetic Wave in a Box System

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    The chiral magnetic wave from the interplay between the chiral magnetic effect and the chiral separation effect is simulated in a box system with the periodic boundary condition based on the chiral kinetic equations of motion. Simulation results are compared with available limits from theoretical derivations, and effects of the temperature, the magnetic field, and the specific shear viscosity on the key properties of the chiral magnetic wave are discussed. Our study serves as a baseline for further simulations of chiral anomalies in relativistic heavy-ion collisions.Comment: 7 pages, 5 figure

    Strange Quark Stars as Probe of Dark Matter

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    We demonstrate that the observation of old strange quark stars (SQSs) can set important limits on the scattering cross sections Οƒq\sigma_q between the light quarks and the non-interacting scalar dark matter (DM). By analyzing a set of 1403 of solitary pulsarlike compact stars in the Milky Way, we find the old solitary pulsar PSR J1801-0857D can set the most stringent upper limits on Οƒq\sigma_q or the DM-proton scattering cross sections Οƒp\sigma_p. By converting Οƒq\sigma_q into Οƒp\sigma_p based on effective operator analyses, we show the resulting Οƒp\sigma_p limit by assuming PSR J1801-0857D to be a SQS could be comparable with that of the current direct detection experiments but much weaker (by several orders of magnitude) than that obtained by assuming PSR J1801-0857D to be a neutron star (NS), which requires an extremely small Οƒp\sigma_p far beyond the limits of direct detection experiments. Our findings imply that the old pulsars are favored to be SQSs rather than NSs if the scalar DM were observed by future terrestrial experiments.Comment: 6 pages, 4 figures. Some results updated and discussions added. Accepted version to appear in Ap

    Simulating chiral anomalies with spin dynamics

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    Considering that the chiral kinetic equations of motion (CEOM) can be derived from the spin kinetic equations of motion (SEOM) for massless particles with approximations, we simulate the chiral anomalies by using the latter in a box system with the periodic boundary condition under a uniform external magnetic field. We found that the chiral magnetic effect is weaker while the damping of the chiral magnetic wave is stronger from the SEOM compared with that from the CEOM. In addition, effects induced by chiral anomalies from the SEOM are less sensitive to the decay of the magnetic field than from the CEOM due to the spin relaxation process.Comment: 6 pages, 6 figure

    Giant Magnons and Spiky Strings on S^3 with B-field

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    We study solutions for a rotating string on S^3 with a background NS-NS B-field and show the existence of spiky string and giant magnon as two limiting solutions. We make a connection to the sine-Gordon model via the Polyakov worldsheet action and study the effect of B-field. In particular, we find the magnon solution can be mapped to the excitation of a fractional spin chain. We conjecture a B-deformed SYM to be the gauge theory dual to this background.Comment: 22 pages, 3 figures, more references adde

    Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation

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    We propose the BinaryGAN, a novel generative adversarial network (GAN) that uses binary neurons at the output layer of the generator. We employ the sigmoid-adjusted straight-through estimators to estimate the gradients for the binary neurons and train the whole network by end-to-end backpropogation. The proposed model is able to directly generate binary-valued predictions at test time. We implement such a model to generate binarized MNIST digits and experimentally compare the performance for different types of binary neurons, GAN objectives and network architectures. Although the results are still preliminary, we show that it is possible to train a GAN that has binary neurons and that the use of gradient estimators can be a promising direction for modeling discrete distributions with GANs. For reproducibility, the source code is available at https://github.com/salu133445/binarygan
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