Seen and unseen tidal caustics in the Andromeda galaxy

Indirect detection of high-energy particles from dark matter interactions is a promising avenue for learning more about dark matter, but is hampered by the frequent coincidence of high-energy astrophysical sources of such particles with putative high-density regions of dark matter. We calculate the boost factor and gamma-ray flux from dark matter associated with two shell-like caustics of luminous tidal debris recently discovered around the Andromeda galaxy, under the assumption that dark matter is its own supersymmetric antiparticle. These shell features could be a good candidate for indirect detection of dark matter via gamma rays because they are located far from the primary confusion sources at the galaxy's center, and because the shapes of the shells indicate that most of the mass has piled up near apocenter. Using a numerical estimator specifically calibrated to estimate densities in N-body representations with sharp features and a previously determined N-body model of the shells, we find that the largest boost factors do occur in the shells but are only a few percent. We also find that the gamma-ray flux is an order of magnitude too low to be detected with Fermi for likely dark matter parameters, and about 2 orders of magnitude less than the signal that would have come from the dwarf galaxy that produces the shells in the N-body model. We further show that the radial density profiles and relative radial spacing of the shells, in either dark or luminous matter, is relatively insensitive to the details of the potential of the host galaxy but depends in a predictable way on the velocity dispersion of the progenitor galaxy.Comment: ApJ accepte

Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort