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Register a new userPulsar glitches: The crust is not enough
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Pulsar glitches are traditionally viewed as a manifestation of vortex dynamics associated with a neutron superfluid reservoir confined to the inner crust of the star. In this Letter we show that the non-dissipative entrainment coupling between the neutron superfluid and the nuclear lattice leads to a less mobile crust superfluid, effectively reducing the moment of inertia associated with the angular momentum reservoir. Combining the latest observational data for prolific glitching pulsars with theoretical results for the crust entrainment we find that the required superfluid reservoir exceeds that available in the crust. This challenges our understanding of the glitch phenomenon, and we discuss possible resolutions to the problem.
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We demonstrate that observations of glitches in the Vela pulsar can be used to investigate the strength of the crust-core coupling in a neutron star, and suggest that recovery from the glitch is dominated by torque exerted by the re-coupling of superfluid components of the core that were decoupled from the crust during the glitch. Assuming that the recoupling is mediated by mutual friction between the superfluid neutrons and the charged components of the core, we use the observed magnitudes and timescales of the shortest timescale components of the recoveries from two recent glitches in the Vela pulsar to infer the fraction of the core that is coupled to the crust during the glitch, and hence spun up by the glitch event. Within the framework of a two-fluid hydrodynamic model of glitches, we analyze whether crustal neutrons alone are sufficient to drive the glitch activity observed in the Vela pulsar. We use two sets of neutron star equations of state (EOSs), both of which span crust and core consistently and cover a range of the slope of the symmetry energy at saturation density $30 < L <120$ MeV. One set produces maximum masses $approx$2.0$M_{odot}$, the second $approx$2.6$M_{odot}$. We also include the effects of entrainment of crustal neutrons by the superfluid lattice. We find that for medium to stiff EOSs, observations imply $>70%$ of the moment of inertia of the core is coupled to the crust during the glitch, though for softer EOSs $Lapprox 30$MeV as little as $5%$ could be coupled. No EOS is able to reproduce the observed glitch activity with crust neutrons alone, but extending the region where superfluid vortices are strongly pinned into the core by densities as little as 0.016fm$^{-3}$ above the crust-core transition density restores agreement with the observed glitch activity.
Numerous spherical ``shells have been observed in young star-forming environments that host low- and intermediate-mass stars. These observations suggest that these shells may be produced by isotropic stellar wind feedback from young main-sequence stars. However, the driving mechanism for these shells remains uncertain because the momentum injected by winds is too low to explain their sizes and dynamics due to their low mass-loss rates. However, these studies neglect how the wind kinetic energy is transferred to the ISM and instead assume it is instantly lost via radiation, suggesting that these shells are momentum-driven. Intermediate-mass stars have fast ($v_w gtrsim 1000$ km/s) stellar winds and therefore the energy injected by winds should produce energy-driven adiabatic wind bubbles that are larger than momentum-driven wind bubbles. Here, we explore if energy-driven wind feedback can produce the observed shells by performing a series of 3D magneto-hydrodynamic simulations of wind feedback from intermediate-mass and high-mass stars that are placed in a magnetized, turbulent molecular cloud. We find that, for the high-mass stars modeled, energy-driven wind feedback produces $sim$pc scale wind bubbles in molecular clouds that agree with the observed shell sizes but winds from intermediate-mass stars can not produce similar shells because of their lower mass-loss rates and velocities. Therefore, such shells must be driven by other feedback processes inherent to low- and intermediate-mass star formation.
Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively assess and compare the performance of statistical models is given by the likelihood. One class of statistical models which has recently gained increasing popularity and has been applied to a variety of complex data are deep belief networks. Analyses of these models, however, have been typically limited to qualitative analyses based on samples due to the computationally intractable nature of the model likelihood. Motivated by these circumstances, the present article provides a consistent estimator for the likelihood that is both computationally tractable and simple to apply in practice. Using this estimator, a deep belief network which has been suggested for the modeling of natural image patches is quantitatively investigated and compared to other models of natural image patches. Contrary to earlier claims based on qualitative results, the results presented in this article provide evidence that the model under investigation is not a particularly good model for natural images
Many structured prediction tasks in machine vision have a collection of acceptable answers, instead of one definitive ground truth answer. Segmentation of images, for example, is subject to human labeling bias. Similarly, there are multiple possible pixel values that could plausibly complete occluded image regions. State-of-the art supervised learning methods are typically optimized to make a single test-time prediction for each query, failing to find other modes in the output space. Existing methods that allow for sampling often sacrifice speed or accuracy. We introduce a simple method for training a neural network, which enables diverse structured predictions to be made for each test-time query. For a single input, we learn to predict a range of possible answers. We compare favorably to methods that seek diversity through an ensemble of networks. Such stochastic multiple choice learning faces mode collapse, where one or more ensemble members fail to receive any training signal. Our best performing solution can be deployed for various tasks, and just involves small modifications to the existing single-mode architecture, loss function, and training regime. We demonstrate that our method results in quantitative improvements across three challenging tasks: 2D image completion, 3D volume estimation, and flow prediction.
We develop a minimal model for textit{pulsar glitches} by introducing a solid-crust potential in the three-dimensional (3D) Gross-Pitaevskii-Poisson equation (GPPE), which we have used earlier to study gravitationally bound Bose-Einstein Condensates (BECs), i.e., bosonic stars. In the absence of the crust potential, we show that, if we rotate such a bosonic star, it is threaded by vortices. We then show, via extensive direct numerical simulations (DNSs), that the interaction of these vortices with the crust potential yields (a) stick-slip dynamics and (b) dynamical glitches. We demonstrate that, if enough momentum is transferred to the crust from the bosonic star, then the vortices are expelled from the star and the crusts angular momentum $J_c$ exhibits features that can be interpreted naturally as glitches. From the time series of $J_c$, we compute the cumulative probability distribution functions (CPDFs) of event sizes, event durations, and waiting times. We show that these CPDFs have signatures of self-organized criticality (SOC), which have been seen in observations on pulsar glitches.
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