A robust boson dispenser: Quantum state preparation in interacting many-particle systems

We present a technique to control the spatial state of a small cloud of interacting particles at low temperatures with almost perfect fidelity using spatial adiabatic passage. To achieve this, the resonant trap energies of the system are engineered in such a way that a single, well-defined eigenstate connects the initial and desired states and is isolated from the rest of the spectrum. We apply this procedure to the task of separating a well-defined number of particles from an initial cloud and show that it can be implemented in radio-frequency traps using experimentally realistic parameters.Comment: 10 pages, 9 figure

Mass Volume Curves and Anomaly Ranking

This paper aims at formulating the issue of ranking multivariate unlabeled observations depending on their degree of abnormality as an unsupervised statistical learning task. In the 1-d situation, this problem is usually tackled by means of tail estimation techniques: univariate observations are viewed as all the more `abnormal' as they are located far in the tail(s) of the underlying probability distribution. It would be desirable as well to dispose of a scalar valued `scoring' function allowing for comparing the degree of abnormality of multivariate observations. Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density. We first study the statistical estimation of the MV curve of a given scoring function and we provide a strategy to build confidence regions using a smoothed bootstrap approach. Optimization of this functional criterion over the set of piecewise constant scoring functions is next tackled. This boils down to estimating a sequence of empirical minimum volume sets whose levels are chosen adaptively from the data, so as to adjust to the variations of the optimal MV curve, while controling the bias of its approximation by a stepwise curve. Generalization bounds are then established for the difference in sup norm between the MV curve of the empirical scoring function thus obtained and the optimal MV curve

Twonniers: Interaction-induced effects on Bose-Hubbard parameters

We study the effects of the repulsive on-site interactions on the broadening of the localized Wannier functions used for calculating the parameters to describe ultracold atoms in optical lattices. For this, we replace the common single-particle Wannier functions, which do not contain any information about the interactions, by two-particle Wannier functions ("Twonniers") obtained from an exact solution which takes the interactions into account. We then use these interaction-dependent basis functions to calculate the Bose--Hubbard model parameters, showing that they are substantially different both at low and high lattice depths, from the ones calculated using single-particle Wannier functions. Our results suggest that density effects are not negligible for many parameter ranges and need to be taken into account in metrology experiments.Comment: 6 pages, 3 figure

XSIL: Extensible Scientific Interchange Language

We motivate and define the XSIL language as a flexible, hierarchical, extensible transport language for scientific data objects. The entire object may be represented in the file, or there may be metadata in the XSIL file, with a powerful, fault-tolerant linking mechanism to external data. The language is based on XML, and is designed not only for parsing and processing by machines, but also for presentation to humans through web browsers and web-database technology. There is a natural mapping between the elements of the XSIL language and the object model into which they are translated by the parser. As well as common objects (Parameter, Array, Time, Table), we have extended XSIL to include the IGWDFrame, used by gravitational-wave observatories

Transport of ultracold atoms between concentric traps via spatial adiabatic passage

Spatial adiabatic passage processes for ultracold atoms trapped in tunnel-coupled cylindrically symmetric concentric potentials are investigated. Specifically, we discuss the matter-wave analogue of the rapid adiabatic passage (RAP) technique for a high fidelity and robust loading of a single atom into a harmonic ring potential from a harmonic trap, and for its transport between two concentric rings. We also consider a system of three concentric rings and investigate the transport of a single atom between the innermost and the outermost rings making use of the matter-wave analogue of the stimulated Raman adiabatic passage (STIRAP) technique. We describe the RAP-like and STIRAP-like dynamics by means of a two- and a three-state models, respectively, obtaining good agreement with the numerical simulations of the corresponding two-dimensional Schr\"odinger equation.Comment: 13 pages, 6 figure

Information Measures: the Curious Case of the Binary Alphabet

Four problems related to information divergence measures defined on finite alphabets are considered. In three of the cases we consider, we illustrate a contrast which arises between the binary-alphabet and larger-alphabet settings. This is surprising in some instances, since characterizations for the larger-alphabet settings do not generalize their binary-alphabet counterparts. Specifically, we show that $f$-divergences are not the unique decomposable divergences on binary alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature. We also show that KL divergence is the unique Bregman divergence which is also an $f$-divergence for any alphabet size. We show that KL divergence is the unique Bregman divergence which is invariant to statistically sufficient transformations of the data, even when non-decomposable divergences are considered. Like some of the problems we consider, this result holds only when the alphabet size is at least three.Comment: to appear in IEEE Transactions on Information Theor

Gamma-ray emission from globular clusters

Over the last few years, the data obtained using the Large Area Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope has provided new insights on high-energy processes in globular clusters, particularly those involving compact objects such as Millisecond Pulsars (MSPs). Gamma-ray emission in the 100 MeV to 10 GeV range has been detected from more than a dozen globular clusters in our galaxy, including 47 Tucanae and Terzan 5. Based on a sample of known gamma-ray globular clusters, the empirical relations between gamma-ray luminosity and properties of globular clusters such as their stellar encounter rate, metallicity, and possible optical and infrared photon energy densities, have been derived. The measured gamma-ray spectra are generally described by a power law with a cut-off at a few gigaelectronvolts. Together with the detection of pulsed gamma-rays from two MSPs in two different globular clusters, such spectral signature lends support to the hypothesis that gamma-rays from globular clusters represent collective curvature emission from magnetospheres of MSPs in the clusters. Alternative models, involving Inverse-Compton (IC) emission of relativistic electrons that are accelerated close to MSPs or pulsar wind nebula shocks, have also been suggested. Observations at >100 GeV by using Fermi/LAT and atmospheric Cherenkov telescopes such as H.E.S.S.-II, MAGIC-II, VERITAS, and CTA will help to settle some questions unanswered by current data.Comment: 11 pages, 7 figures, 2 tables, J. Astron. Space Sci., in pres

Calibration of One-Class SVM for MV set estimation

A general approach for anomaly detection or novelty detection consists in estimating high density regions or Minimum Volume (MV) sets. The One-Class Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating such regions from high dimensional data. Yet it suffers from practical limitations. When applied to a limited number of samples it can lead to poor performance even when picking the best hyperparameters. Moreover the solution of OCSVM is very sensitive to the selection of hyperparameters which makes it hard to optimize in an unsupervised setting. We present a new approach to estimate MV sets using the OCSVM with a different choice of the parameter controlling the proportion of outliers. The solution function of the OCSVM is learnt on a training set and the desired probability mass is obtained by adjusting the offset on a test set to prevent overfitting. Models learnt on different train/test splits are then aggregated to reduce the variance induced by such random splits. Our approach makes it possible to tune the hyperparameters automatically and obtain nested set estimates. Experimental results show that our approach outperforms the standard OCSVM formulation while suffering less from the curse of dimensionality than kernel density estimates. Results on actual data sets are also presented.Comment: IEEE DSAA' 2015, Oct 2015, Paris, Franc