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