1,759 research outputs found
Fermionization of two distinguishable fermions
In this work we study a system of two distinguishable fermions in a 1D
harmonic potential. This system has the exceptional property that there is an
analytic solution for arbitrary values of the interparticle interaction. We
tune the interaction strength via a magnetic offset field and compare the
measured properties of the system to the theoretical prediction. At the point
where the interaction strength diverges, the energy and square of the wave
function for two distinguishable particles are the same as for a system of two
identical fermions. This is referred to as fermionization. We have observed
this phenomenon by directly comparing two distinguishable fermions with
diverging interaction strength with two identical fermions in the same
potential. We observe good agreement between experiment and theory. By adding
one or more particles our system can be used as a quantum simulator for more
complex few-body systems where no theoretical solution is available
LARES/WEBER-SAT and the equivalence principle
It has often been claimed that the proposed Earth artificial satellite
LARES/WEBER-SAT-whose primary goal is, in fact, the measurement of the general
relativistic Lense-Thirring effect at a some percent level-would allow to
greatly improve, among (many) other things, the present-day (10^-13) level of
accuracy in testing the equivalence principle as well. Recent claims point
towards even two orders of magnitude better, i.e. 10^-15. In this note we show
that such a goal is, in fact, unattainable by many orders of magnitude being,
instead, the achievable level of the order of 10^-9.Comment: LaTex, 4 pages, no figures, no tables, 26 references. Proofs
corrections included. To appear in EPL (Europhysics Letters
On the possibility of measuring relativistic gravitational effects with a LAGEOS-LAGEOS II-OPTIS-mission
In this paper we wish to preliminary investigate if it would be possible to
use the orbital data from the proposed OPTIS mission together with those from
the existing geodetic passive SLR LAGEOS and LAGEOS II satellites in order to
perform precise measurements of some general relativistic
gravitoelectromagnetic effects, with particular emphasis on the Lense-Thirring
effect.Comment: Abridged version. 16 pages, no figures, 1 table. First results from
the GGM01C Earth gravity model. GRACE data include
Analytical Solution for the Current Distribution in Multistrand Superconducting Cables
Current distribution in multistrand superconducting cables can be a major concern for stability in superconducting magnets and for field quality in particle accelerator magnets. In this paper we describe multistrand superconducting cables by means of a distributed parameters circuit model. We derive a system of partial differential equations governing current distribution in the cable and we give the analytical solution of the general system. We then specialize the general solution to the particular case of uniform cable properties. In the particular case of a two-strand cable, we show that the analytical solution presented here is identical to the one already available in the literature. For a cable made of N equal strands we give a closed form solution that to our knowledge was never presented before. We finally validate the analytical solution by comparison to numerical results in the case of a step-like spatial distribution of the magnetic field over a short Rutherford cable, both in transient and steady state conditions
Solid deuterium surface degradation at ultracold neutron sources
Solid deuterium (sD_2) is used as an efficient converter to produce ultracold
neutrons (UCN). It is known that the sD_2 must be sufficiently cold, of high
purity and mostly in its ortho-state in order to guarantee long lifetimes of
UCN in the solid from which they are extracted into vacuum. Also the UCN
transparency of the bulk sD_2 material must be high because crystal
inhomogeneities limit the mean free path for elastic scattering and reduce the
extraction efficiency. Observations at the UCN sources at Paul Scherrer
Institute and at Los Alamos National Laboratory consistently show a decrease of
the UCN yield with time of operation after initial preparation or later
treatment (`conditioning') of the sD_2. We show that, in addition to the
quality of the bulk sD_2, the quality of its surface is essential. Our
observations and simulations support the view that the surface is deteriorating
due to a build-up of D_2 frost-layers under pulsed operation which leads to
strong albedo reflections of UCN and subsequent loss. We report results of UCN
yield measurements, temperature and pressure behavior of deuterium during
source operation and conditioning, and UCN transport simulations. This,
together with optical observations of sD_2 frost formation on initially
transparent sD_2 in offline studies with pulsed heat input at the North
Carolina State University UCN source results in a consistent description of the
UCN yield decrease.Comment: 15 pages, 22 figures, accepted by EPJ-
Folding Langmuir Monolayers
The maximum pressure a two-dimensional surfactant monolayer is able to
withstand is limited by the collapse instability towards formation of
three-dimensional material. We propose a new description for reversible
collapse based on a mathematical analogy between the formation of folds in
surfactant monolayers and the formation of Griffith Cracks in solid plates
under stress. The description, which is tested in a combined microscopy and
rheology study of the collapse of a single-phase Langmuir monolayer of
2-hydroxy-tetracosanoic acid (2-OH TCA), provides a connection between the
in-plane rheology of LM's and reversible folding
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
There is a rising need for computational models that can complementarily
leverage data of different modalities while investigating associations between
subjects for population-based disease analysis. Despite the success of
convolutional neural networks in representation learning for imaging data, it
is still a very challenging task. In this paper, we propose a generalizable
framework that can automatically integrate imaging data with non-imaging data
in populations for uncertainty-aware disease prediction. At its core is a
learnable adaptive population graph with variational edges, which we
mathematically prove that it is optimizable in conjunction with graph
convolutional neural networks. To estimate the predictive uncertainty related
to the graph topology, we propose the novel concept of Monte-Carlo edge
dropout. Experimental results on four databases show that our method can
consistently and significantly improve the diagnostic accuracy for Autism
spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its
generalizability in leveraging multimodal data for computer-aided diagnosis.Comment: Accepted to MICCAI 202
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