Binary neutron star mergers: a jet engine for short gamma-ray bursts

We perform magnetohydrodynamic simulations in full general relativity (GRMHD) of quasi-circular, equal-mass, binary neutron stars that undergo merger. The initial stars are irrotational, $n=1$ polytropes and are magnetized. We explore two types of magnetic-field geometries: one where each star is endowed with a dipole magnetic field extending from the interior into the exterior, as in a pulsar, and the other where the dipole field is initially confined to the interior. In both cases the adopted magnetic fields are initially dynamically unimportant. The merger outcome is a hypermassive neutron star that undergoes delayed collapse to a black hole (spin parameter $a/M_{\rm BH} \sim 0.74$) immersed in a magnetized accretion disk. About $4000M \sim 60(M_{\rm NS}/1.625M_\odot)$ ms following merger, the region above the black hole poles becomes strongly magnetized, and a collimated, mildly relativistic outflow --- an incipient jet --- is launched. The lifetime of the accretion disk, which likely equals the lifetime of the jet, is $\Delta t \sim 0.1 (M_{\rm NS}/1.625M_\odot)$ s. In contrast to black hole--neutron star mergers, we find that incipient jets are launched even when the initial magnetic field is confined to the interior of the stars.Comment: 6 pages, 3 figures, 1 table, matches published versio

Energy time dispersion of a new class of magnetospheric ion events observed near the Earth's bow shock

International audienceWe have analyzed high time resolution (\geq6 s) data during the onset and the decay phase of several energetic (\geq35 keV) ion events observed near the Earth's bow shock by the CCE/AMPTE and IMP-7/8 spacecraft, during times of intense substorm/geomagnetic activity. We found that forward energy dispersion at the onset of events (earlier increase of middle energy ions) and/or a delayed fall of the middle energy ion fluxes at the end of events are often evident in high time resolution data. The energy spectra at the onset and the decay of this kind of events show a characteristic hump at middle (50-120 keV) energies and the angular distributions display either anisotropic or broad forms. The time scale of energy dispersion in the ion events examined was found to range from several seconds to \sim1 h depending on the ion energies compared and on the rate of variation of the Interplanetary Magnetic Field (IMF) direction. Several canditate processes are discussed to explain the observations and it is suggested that a rigidity dependent transport process of magnetospheric particles within the magnetosheath is most probably responsible for the detection of this new type of near bow shock magnetospheric ion events. The new class of ion events was observed within both the magnetosheath and the upstream region

Optimization of Multiclass Queueing Networks: Polyhedral and Nonlinear Characterizations of Achievable Performance

We consider open and closed multiclass queueing networks with Poisson arrivals (in open networks), exponentially distributed class dependent service times, and with class dependent deterministic or probabilistic routing. For open networks, the performance objective is to minimize, over all sequencing and routing policies, a weighted sum of the expected response times of different classes. Using a powerful technique involving quadratic or higher order potential functions, we propose variants of a method to derive polyhedral and nonlinear spaces which contain the entire set of achievable response times under stable and preemptive scheduling policies. By optimizing over these spaces, we obtain lower bounds on achievable performance. In particular, we obtain a sequence of progressively more complicated nonlinear approximations (relaxations) which are progressively closer to the exact achievable space. In the special case of single station networks (multiclass queues and Klimov's model) and homogenous multiclass networks, our characterization gives exactly the achievable region. Consequently, the proposed method can be viewed as the natural extension of conservation laws to multiclass queueing networks. For closed networks, the performance objective is to maximize throughput. We similarly find polyhedral and nonlinear spaces that include the performance space and by maximizing over these spaces we obtain an upper bound on the optimal throughput. We check the tightness of our bounds by simulating heuristic scheduling policies for simple open networks and we find that the first order approximation of our method is at least as good as simulation-based existing methods. In terms of computational complexity and in contrast to simulation-based existing methods, the calculation of our first order bounds consists of solving a linear programming problem with both the number of variables and constraints being polynomial (quadratic) in the number of classes in the network. The i-th order approximation involves solving a convex programming problem in dimension O(Ri+l), where R is the number of classes in the network, which can be solved efficiently using techniques from semi-definite programming

Binary Black-Hole Mergers in Magnetized Disks: Simulations in Full General Relativity

We present results from the first fully general relativistic, magnetohydrodynamic (GRMHD) simulations of an equal-mass black hole binary (BHBH) in a magnetized, circumbinary accretion disk. We simulate both the pre and post-decoupling phases of a BHBH-disk system and both "cooling" and "no-cooling" gas flows. Prior to decoupling, the competition between the binary tidal torques and the effective viscous torques due to MHD turbulence depletes the disk interior to the binary orbit. However, it also induces a two-stream accretion flow and mildly relativistic polar outflows from the BHs. Following decoupling, but before gas fills the low-density "hollow" surrounding the remnant, the accretion rate is reduced, while there is a prompt electromagnetic (EM) luminosity enhancement following merger due to shock heating and accretion onto the spinning BH remnant. This investigation, though preliminary, previews more detailed GRMHD simulations we plan to perform in anticipation of future, simultaneous detections of gravitational and EM radiation from a merging BHBH-disk system.Comment: 5 pages, 5 figure

Plasma IMS Composition Measurements for Europa and Ganymede

NASA and ESA are planning the joint Europa Jupiter System Mission (EJSM) to the Jupiter system with specific emphasis to Europa and Ganymede, respectively. The Japanese Space Agency is also planning an orbiter mission to explore Jupiter's magnetosphere and the Galilean satellites. For NASA's Jupiter Europa Orbiter (JEO) we are developing the 3D Ion Mass Spectrometer (IMS) with two main goals which can also be applied to the other Galilean moons, 1) measure the plasma interaction between Europa and Jupiter's magnetosphere and 2) infer the 4n surface composition to trace elemental [1] and significant isotopic levels. The first goal supports the magnetometer (MAG) measurements, primarily directed at detection of Europa's sub-surface ocean, while the second gives information about transfer of material between the Galilean moons, and between the moon surfaces and subsurface layers putatively including oceans. The measurement of the interactions for all the Galilean moons can be used to trace the in situ ion measurements of pickup ions back to either Europa's or Ganymede's surface from the respectively orbiting spacecraft. The IMS instrument, being developed under NASA's Astrobiology Instrument Development Program, would maximally achieve plasma measurement requirements for JEO and EJSM while moving forward our knowledge of Jupiter system composition and source processes to far higher levels than previously envisaged

Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers

Learning from humans: combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task

We develop a method to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL), using on-policy algorithms that are more suitable to learn from pre-trained networks. We show that the combination of IL and RL can match human results and that good performance strongly depends on an egocentric representation of the environment. The developed methodology can be used to efficiently learn policies for unmanned vehicles which have to solve missions in an open field environment.Comment: 24 pages, 15 figure

Photoacoustic study of a new neodymium(III) hydrazone complex

The neodymium(III) hydrazone complex [Nd(DBH) 2 (NO 3 ) 3 ] has been synthesized and characterized using microanalysis and IR spectroscopy. High-resolution photoacoustic spectrometry has been applied for studying the complex. The obtained photoacoustic spectrum has been analysed and compared to a similar [Nd(PicBH) 2 (NO 3 ) 2 ]NO 3 complex. The intensities of the f-f transitions in the photoacoustic spectrum of Nd(DBH) 2 (NO 3 ) 3 were two times greater than for the [Nd(PicBH) 2 (NO 3 ) 2 ]NO 3 complex

Human-like Decision Making and Motion Control for Smooth and Natural Car Following

Car-following is an important driving behaviour for intelligent vehicles and has a significant impact on traffic efficiency and traffic safety. Car-following models are widely developed to characterize the human-drivers car-following manoeuvre actions and adopted in traffic simulation and automated vehicle control system development. Car-following models need to be able to represent the drivers behaviour while following preceding vehicles. On the other hand, car-following controllers are an important component of intelligent vehicle systems, both for autonomous vehicles and connected vehicles. However, Adaptive Cruise Control (ACC) as well as Cooperative Adaptive Cruise Control (CACC) do not include human behaviour, which makes their car-following behaviour not human-like or natural for the on-board driver or passenger. To address this problem, in this study, the human-like Wiedemann car-following model is calibrated and verified with our driving simulator data. A human-like car-following nonlinear model predictive control (MPC) controller is developed based on the calibrated car-following model. Three different scenarios are tested to evaluate the performance of the proposed controller, with which the autonomous vehicle is able to have human-like and smooth trajectories at different phases and within different transition zones

Fisk-Gloeckler Suprathermal Proton Spectrum in the Heliosheath and the Local Interstellar Medium

Convergence of suprathermal keV-MeV proton and ion spectra approximately to the Fisk-Gloeckler (F-G) form j(E) = j(sub 0) E(sup -1.5) in Voyager land 2 heliosheath measurements is suggestive of distributed acceleration in Kolmogorov turbulence which may extend well beyond the heliopause into the local interstellar medium (LISM). Turbulence of this type is already indicated by interstellar radio scintillation measurements of electron density power spectra. Previously published extrapolations (Cooper et al., 2003, 2006) of the LISM proton spectrum from eV to GeV energies are highly consistent with the F-G power-law and further indicative of such turbulence and LISM effectiveness of the F-G cascade acceleration process. The LISM pressure computed from this spectrum well exceeds that from current estimates for the LISM magnetic field, so exchange of energy between the protons and the magnetic field would likely have a strong role in evolution of the turbulence as per the F-G theory and as long ago proposed for cosmic ray energies by Parker and others. Pressure-dependent estimates of the LISM field strength should not ignore this potentially strong and even dominant contribution from the plasma. Presence of high-beta suprathermal plasma on LISM field lines could significantly affect interactions with the heliospheric outer boundary region and might potentially account for distributed and more discrete features in ongoing measurements of energetic neutral emission from the Interstellar Boundary Explorer (IBEX) mission