Anomaly-Induced Magnetic Screening in 2+1 dimensional QED at Finite Density

We show that in 2+1 dimensional Quantum Electrodynamics an external magnetic field applied to a finite density of massless fermions is screened, due to a $2+1$-dimensional realization of the underlying $2$-dimensional axial anomaly of the space components of the electric current. This is shown to imply screening of the magnetic field, i.e., the Meissner effect. We discuss the physical implications of this result.Comment: 8 pages, DFTT-93-10 [ Eq.(15) and (16) were scrambled in previous version

Large-scale study of the NGC 1399 globular cluster system in Fornax

We present a Washington C and Kron-Cousins R photometric study of the globular cluster system of NGC 1399, the central galaxy of the Fornax cluster. A large areal coverage of 1 square degree around NGC 1399 is achieved with three adjoining fields of the MOSAIC II Imager at the CTIO 4-m telescope. Working on such a large field, we can perform the first indicative determination of the total size of the NGC 1399 globular cluster system. The estimated angular extent, measured from the NGC 1399 centre and up to a limiting radius where the areal density of blue globular clusters falls to 30 per cent of the background level, is 45 +/- 5 arcmin, which corresponds to 220 - 275 kpc at the Fornax distance. The bimodal colour distribution of this globular cluster system, as well as the different radial distribution of blue and red clusters, up to these large distances from the parent galaxy, are confirmed. The azimuthal globular cluster distribution exhibits asymmetries that might be understood in terms of tidal stripping of globulars from NGC 1387, a nearby galaxy. The good agreement between the areal density profile of blue clusters and a projected dark-matter NFW density profile is emphasized.Comment: 9 pages, 9 figures. Accepted for publication in A&

Neural Network Parametrization of Deep-Inelastic Structure Functions

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F_2 of the proton and deuteron, and a precision determination of the isotriplet combination F_2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.Comment: Latex, 43 pages, 22 figures. (v2) Final version, published in JHEP; Sect.5.2 and Fig.9 improved, a few typos corrected and other minor improvements. (v3) Some inconsequential typos in Tab.1 and Tab 5 corrected. Neural parametrization available at http://sophia.ecm.ub.es/f2neura