Correlated Prompt Fission Data in Transport Simulations

Detailed information on the fission process can be inferred from the observation, modeling and theoretical understanding of prompt fission neutron and $\gamma$-ray~observables. Beyond simple average quantities, the study of distributions and correlations in prompt data, e.g., multiplicity-dependent neutron and \gray~spectra, angular distributions of the emitted particles, $n$-$n$, $n$-$\gamma$, and $\gamma$-$\gamma$~correlations, can place stringent constraints on fission models and parameters that would otherwise be free to be tuned separately to represent individual fission observables. The FREYA~and CGMF~codes have been developed to follow the sequential emissions of prompt neutrons and $\gamma$-rays~from the initial excited fission fragments produced right after scission. Both codes implement Monte Carlo techniques to sample initial fission fragment configurations in mass, charge and kinetic energy and sample probabilities of neutron and $\gamma$~emission at each stage of the decay. This approach naturally leads to using simple but powerful statistical techniques to infer distributions and correlations among many observables and model parameters. The comparison of model calculations with experimental data provides a rich arena for testing various nuclear physics models such as those related to the nuclear structure and level densities of neutron-rich nuclei, the $\gamma$-ray~strength functions of dipole and quadrupole transitions, the mechanism for dividing the excitation energy between the two nascent fragments near scission, and the mechanisms behind the production of angular momentum in the fragments, etc. Beyond the obvious interest from a fundamental physics point of view, such studies are also important for addressing data needs in various nuclear applications. (See text for full abstract.)Comment: 39 pages, 57 figure files, published in Eur. Phys. J. A, reference added this versio

Perspectives of Nuclear Physics in Europe: NuPECC Long Range Plan 2010

The goal of this European Science Foundation Forward Look into the future of Nuclear Physics is to bring together the entire Nuclear Physics community in Europe to formulate a coherent plan of the best way to develop the field in the coming decade and beyond.<p></p> The primary aim of Nuclear Physics is to understand the origin, evolution, structure and phases of strongly interacting matter, which constitutes nearly 100% of the visible matter in the universe. This is an immensely important and challenging task that requires the concerted effort of scientists working in both theory and experiment, funding agencies, politicians and the public.<p></p> Nuclear Physics projects are often “big science”, which implies large investments and long lead times. They need careful forward planning and strong support from policy makers. This Forward Look provides an excellent tool to achieve this. It represents the outcome of detailed scrutiny by Europe’s leading experts and will help focus the views of the scientific community on the most promising directions in the field and create the basis for funding agencies to provide adequate support.<p></p> The current NuPECC Long Range Plan 2010 “Perspectives of Nuclear Physics in Europe” resulted from consultation with close to 6 000 scientists and engineers over a period of approximately one year. Its detailed recommendations are presented on the following pages. For the interested public, a short summary brochure has been produced to accompany the Forward Look.<p></p&gt

Kinetic and dynamic data structures for convex hulls and upper envelopes

AbstractLet S be a set of n moving points in the plane. We present a kinetic and dynamic (randomized) data structure for maintaining the convex hull of S. The structure uses O(n) space, and processes an expected number of O(n2βs+2(n)logn) critical events, each in O(log2n) expected time, including O(n) insertions, deletions, and changes in the flight plans of the points. Here s is the maximum number of times where any specific triple of points can become collinear, βs(q)=λs(q)/q, and λs(q) is the maximum length of Davenport–Schinzel sequences of order s on n symbols. Compared with the previous solution of Basch, Guibas and Hershberger [J. Basch, L.J. Guibas, J. Hershberger, Data structures for mobile data, J. Algorithms 31 (1999) 1–28], our structure uses simpler certificates, uses roughly the same resources, and is also dynamic