Gluon saturation and Feynman scaling in leading neutron production

In this paper we extend the color dipole formalism to the study of leading neutron production in $e + p \rightarrow e + n + X$ collisions at high energies and estimate the related observables, which were measured at HERA and may be analysed in future electron-proton ($ep$) colliders. In particular, we calculate the Feynman $x_F$ distribution of leading neutrons, which is expressed in terms of the pion flux and the photon-pion total cross section. In the color dipole formalism, the photon-pion cross section is described in terms of the dipole-pion scattering amplitude, which contains information about the QCD dynamics at high energies and gluon saturation effects. We consider different models for the scattering amplitude, which have been used to describe the inclusive and diffractive $ep$ HERA data. Moreover, the model dependence of our predictions with the description of the pion flux is analysed in detail. We show that the recently released H1 leading neutron spectra can be reproduced using the color dipole formalism and that these spectra could help us to observe more clearly gluon saturation effects in future $ep$ colliders.Comment: 10 pages, 5 figure

Double vector meson production in the International Linear Collider

In this paper we study double vector meson production in $\gamma \gamma$ interactions at high energies and, using the color dipole picture, estimate the main observables which can be probed at the International Linear Collider (ILC). The total $\gamma (Q_1^2) + \gamma (Q_2^2) \rightarrow V_1 + V_2$ cross-sections for $V_i = \rho$, $\phi$, $J/\psi$ and $\Upsilon$ are computed and the energy and virtuality dependencies are studied in detail. Our results demonstrate that the experimental analysis of this process is feasible at the ILC and it can be useful to constrain the QCD dynamics at high energies.Comment: 11 pages, 8 figure

Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

Validation of Bayesian posterior distributions using a multidimensional Kolmogorov-Smirnov test

We extend the Kolmogorov–Smirnov (K-S) test to multiple dimensions by suggesting a R^n → [0, 1] mapping based on the probability content of the highest probability density region of the reference distribution under consideration; this mapping reduces the problem back to the one-dimensional case to which the standard K-S test may be applied. The universal character of this mapping also allows us to introduce a simple, yet general, method for the validation of Bayesian posterior distributions of any dimensionality. This new approach goes beyond validating software implementations; it provides a sensitive test for all assumptions, explicit or implicit, that underlie the inference. In particular, the method assesses whether the inferred posterior distribution is a truthful representation of the actual constraints on the model parameters. We illustrate our multidimensional K-S test by applying it to a simple two- dimensional Gaussian toy problem, and demonstrate our method for posterior validation in the real-world astrophysical application of estimating the physical parameters of galaxy clusters parameters from their Sunyaev–Zel’dovich effect in microwave background data. In the latter example, we show that the method can validate the entire Bayesian inference process across a varied population of objects for which the derived posteriors are different in each case.This work was supported by the UK Space Agency under grant ST/K003674/1. This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council.This is the author accepted manuscript. The final version is available from Oxford University Press via http://dx.doi.org/10.1093/mnras/stv111

Beam Alignment Techniques Based on the Current Multiplication Effect in Photoconductors Summary Technical Progress Report

Beam alignment techniques based on current multiplication effect in photoconductors for application to spacecraft communications syste

Experimental Observation of Quantum Correlations in Modular Variables

We experimentally detect entanglement in modular position and momentum variables of photon pairs which have passed through $D$-slit apertures. We first employ an entanglement criteria recently proposed in [Phys. Rev. Lett. {\bf 106}, 210501 (2011)], using variances of the modular variables. We then propose an entanglement witness for modular variables based on the Shannon entropy, and test it experimentally. Finally, we derive criteria for Einstein-Podolsky-Rosen-Steering correlations using variances and entropy functions. In both cases, the entropic criteria are more successful at identifying quantum correlations in our data.Comment: 7 pages, 4 figures, comments welcom