1,891 research outputs found
Black Holes as Neutrino Factories
Ultralight bosons can grow substantially in the vicinity of a black hole,
through superradiant energy extraction. Consequently, such bosons can
potentially reach field values close to the Planck scale, making black holes
powerful transducers of such fields. If a scalar field couples to neutrino, it
can trigger parametric production of neutrinos, and potentially quench their
superradiant growth. During this saturation phase, scalar clouds can accelerate
neutrinos to the TeV energy scale, generating fluxes that surpass those
produced by atmospheric neutrinos.Comment: 13 pages, 3 figure
Photon Ring Astrometry for Superradiant Clouds
Gravitational atoms produced from the superradiant extraction of rotational
energy of spinning black holes can reach energy densities significantly higher
than that of dark matter, turning black holes into powerful potential detectors
for ultralight bosons. These structures are formed by coherently oscillating
bosons, which induce oscillating metric perturbations, deflecting photon
geodesics passing through their interior. The deviation of nearby geodesics can
be further amplified near critical bound photon orbits. We discuss the prospect
of detecting this deflection using photon ring autocorrelations with the Event
Horizon Telescope and its next generation upgrade, which can probe a large
unexplored region of the cloud mass parameter space when compared with previous
constraints.Comment: 9 pages, 5 figure
Multimodal Learning of Soft Robot Dynamics using Differentiable Filters
Differentiable Filters, as recursive Bayesian estimators, possess the ability
to learn complex dynamics by deriving state transition and measurement models
exclusively from data. This data-driven approach eliminates the reliance on
explicit analytical models while maintaining the essential algorithmic
components of the filtering process. However, the gain mechanism remains
non-differentiable, limiting its adaptability to specific task requirements and
contextual variations. To address this limitation, this paper introduces an
innovative approach called {\alpha}-MDF (Attention-based Multimodal
Differentiable Filter). {\alpha}-MDF leverages modern attention mechanisms to
learn multimodal latent representations for accurate state estimation in soft
robots. By incorporating attention mechanisms, {\alpha}-MDF offers the
flexibility to tailor the gain mechanism to the unique nature of the task and
context. The effectiveness of {\alpha}-MDF is validated through real-world
state estimation tasks on soft robots. Our experimental results demonstrate
significant reductions in state estimation errors, consistently surpassing
differentiable filter baselines by up to 45% in the domain of soft robotics.Comment: 13 pages, 8 figures, 5 tables, CoRL 2023 workshop Learning for Soft
Robot
Ground-state Properties and Bogoliubov Modes of a Harmonically Trapped One-Dimensional Quantum Droplet
We study the stationary and excitation properties of a one-dimensional
quantum droplet in the two-component Bose mixture trapped in a harmonic
potential. By constructing the energy functional for the inhomogeneous mixture,
we elaborate the extended the Gross-Pitaevskii equation applicable to both
symmetric and asymmetric mixtures into a universal form, and the equations in
two different dimensionless schemes are in a duality relation, i.e. the unique
parameters left are inverse of each other. The Bogoliubov equations for the
trapped droplet are obtained by linearizing the small density fluctuation
around the ground state and the low-lying excitation modes are calculated
numerically.It is found that the confinement trap changes easily the flat-top
structure for large droplets and alters the mean square radius and the chemical
potential intensively. The breathing mode of the confined droplet connects the
self-bound and ideal gas limits, with the excitation in the weakly interacting
Bose condensate for large particle numbers lying in between. We explicitly show
how the continuum spectrum of the excitation is split into discrete modes, and
finally taken over by the harmonic trap. Two critical particle numbers are
identified by the minimum size of the trapped droplet and the maximum breathing
mode energy, both of which are found to decrease exponentially with the
trapping parameter.Comment: 11 pages, 7 figure
- …