Deuteron Photodissociation in Ultraperipheral Relativistic Heavy-Ion on Deuteron Collisions

In ultraperipheral relativistic deuteron on heavy-ion collisions, a photon emitted from the heavy nucleus may dissociate the deuterium ion. We find deuterium breakup cross sections of 1.38 barns for deuterium-gold collisions at a center of mass energy of 200 GeV per nucleon, as studied at the Relativistic Heavy Ion Collider, and 2.49 barns for deuterium-lead collisions at a center of mass energy of 6.2 TeV, as proposed for the Large Hadron Collider. This cross section includes an energy-independent 140 mb contribution from hadronic diffractive dissociation. At the LHC, the cross section is as large as that of hadronic interactions. The estimated error is 5%. Deuteron dissociation could be used as a luminosity monitor and a `tag' for moderate impact parameter collisions.Comment: Final version, to appear in Phys. Rev. C. Diffractive dissociation included 10 pages with 3 figure

Elastic and Raman scattering of 9.0 and 11.4 MeV photons from Au, Dy and In

Monoenergetic photons between 8.8 and 11.4 MeV were scattered elastically and in elastically (Raman) from natural targets of Au, Dy and In.15 new cross sections were measured. Evidence is presented for a slight deformation in the 197Au nucleus, generally believed to be spherical. It is predicted, on the basis of these measurements, that the Giant Dipole Resonance of Dy is very similar to that of 160Gd. A narrow isolated resonance at 9.0 MeV is observed in In.Comment: 31 pages, 11 figure

Neutron production by 200 mJ ultrashort laser pulses

We report the observation of neutrons rf:leased from d(d,n) He-3 fusion reactions in the focus of 200 mJ, 160 fs Ti:sapphire laser pulses on a deuterated polyethylene target. Optimizing the fast electron and ion generation by applying a well-defined prepulse led to an average rate of 140 neutrons per shot. Furthermore, the production of a substantial number of MeV gamma rays could be observed. The occurrence of neutrons and gamma rays is attributed to the formation and explosion of a relativistic plasma channel in the laser focus, which is confirmed by numerical calculations

Combining centrality indices: Maximizing the predictability of keystone species in food webs

Network analysis offers a rich toolkit to study various graph models in biology. In ecology, centrality indices have been suggested to indicate keystone species in interaction networks and to quantify their importance in an ecosystem. There is a large number of centrality indices, however, and it is often unclear what is their precise biological meaning, how are they related to each other and which one is the “best” predicting the functioning of the modelled biological system. It is a major challenge to use simple structural indicators in order to predict the outcome of much more complicated dynamical simulations. The question is which one is the most preidictive one and what is the meaning of particular structural indices. Here we use machine learning techniques to combine k centrality indices out of n in such a way that the gained combined index (a “cocktail” of single indices) correlates better with simulated dynamics. In particular, we are interested in rank correlations between single-node and multi-node centrality and simulated node importance. We identify index families based on similarity. The best single-index correlations (weighted degree centrality) can predict simulated food web dynamics with an accuracy up to 70.06%. This accuracy can be raised reasonably, using the best cocktail, up to 78.42%. This is a combination of node degree (D) and 5-step-long weighted importance index (WI5). Since they have completely different properties (the former is local and binary, the latter is meso-scale and weighted), we can demonstrate that a good cocktail has to combine indices from different families in order to best improve predictions. If one needs to predict dynamics from structure, there is a way to use wise proxies of simple topological indices – instead of performing complicated simulations