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Abstract We write down the force-free electrodynamics equations in dipole coordinates and solve for axisymmetric normal modes corresponding to Alfvénic perturbations in the magnetosphere of a neutron star. We show that a single Alfvén wave propagating on dipole field lines spontaneously sources a fast magnetosonic (fms) wave at the next order in the perturbation expansion, without needing three-wave interaction. The frequency of the sourced fms wave is twice the original Alfvén wave frequency, and the wave propagates spherically outward. The properties of the outgoing fms wave can be computed exactly using the usual devices of classical electrodynamics. We extend the calculation to the closed zone of a rotating neutron star magnetosphere, and show that the Alfvén wave also sources a spherical fms wave but at the same frequency as the primary Alfvén wave. 

Radiative processes such as synchrotron radiation and Compton scattering play an important role in astrophysics. Radiative processes are fundamentally stochastic in nature, and the best tools currently used for resolving these processes computationally are Monte Carlo (MC) methods. These methods typically draw a large number of samples from a complex distribution such as the differential cross section for electron–photon scattering, and then use these samples to compute the radiation properties such as angular distribution, spectrum, and polarization. In this work, we propose a machine learning (ML) technique for efficient sampling from arbitrary known probability distributions that can be used to accelerate MC calculation of radiative processes in astrophysical scenarios. In particular, we apply our technique to inverse Compton radiation and find that our ML method can be up to an order of magnitude faster than traditional methods currently in use. 

Abstract Black holes can launch powerful jets through the Blandford–Znajek process. This relies on enough plasma in the jet funnel to conduct the necessary current. However, in some low-luminosity active galactic nuclei, the plasma supply near the jet base may be an issue. It has been proposed that spark gaps—local regions with unscreened electric field—can form in the magnetosphere, accelerating particles to initiate pair cascades, thus filling the jet funnel with plasma. In this paper, we carry out 2D general relativistic particle-in-cell (GRPIC) simulations of the gap, including self-consistent treatment of inverse Compton scattering and pair production. We observe gap dynamics that is fully consistent with our earlier 1D GRPIC simulations. We find strong dependence of the gap power on the soft photon spectrum and energy density, as well as the strength of the horizon magnetic field. We derive physically motivated scaling relations, and applying to M87, we find that the gap may be energetically viable for the observed TeV flares. For Sgr A*, the energy dissipated in the gap may also be sufficient to power the X-ray flares. 

Abstract Rapid shear motion of magnetar crust can launch Alfvén waves into the magnetosphere. The dissipation of the Alfvén waves has been theorized to power the X-ray bursts characteristic of magnetars. However, the process by which Alfvén waves convert their energy to X-rays is unclear. Recent work has suggested that energetic fast magnetosonic (fast) waves can be produced as a byproduct of Alfvén waves propagating on curved magnetic field lines; their subsequent dissipation may power X-ray bursts. In this work, we investigate the production of fast waves by performing axisymmetric force-free simulations of Alfvén waves propagating in a dipolar magnetosphere. For Alfvén wave trains that do not completely fill the flux tube confining them, we find a fast wave dominated by a low frequency component with a wavelength defined by the bouncing time of the Alfvén waves. In contrast, when the wave train is long enough to completely fill the flux tube, and the Alfvén waves overlap significantly, the energy is quickly converted into a fast wave with a higher frequency that corresponds to twice the Alfvén wave frequency. We investigate how the energy, duration, and wavelength of the initial Alfvén wave train affect the conversion efficiency to fast waves. For modestly energetic star quakes, we see that the fast waves that are produced will become nonlinear well within the magnetosphere, and we comment on the X-ray emission that one may expect from such events. 

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each “virtual individual”, which we define as each fixed set of model parameters (1) and (2) above. The “virtual population” and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated “virtual population database” of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus–cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h. 

Electrotactile stimulus is a form of sensory substitution in which an electrical signal is perceived as a mechanical sensation. The electrotactile effect could, in principle, recapitulate a range of tactile experience by selective activation of nerve endings. However, the method has been plagued by inconsistency, galvanic reactions, pain and desensitization, and unwanted stimulation of nontactile nerves. Here, we describe how a soft conductive block copolymer, a stretchable layout, and concentric electrodes, along with psychophysical thresholding, can circumvent these shortcomings. These purpose-designed materials, device layouts, and calibration techniques make it possible to generate accurate and reproducible sensations across a cohort of 10 human participants and to do so at ultralow currents (≥6 microamperes) without pain or desensitization. This material, form factor, and psychophysical approach could be useful for haptic devices and as a tool for activation of the peripheral nervous system.