Refine
Year of publication
- 2009 (10) (remove)
Document Type
- Article (10) (remove)
Has Fulltext
- yes (10)
Is part of the Bibliography
- no (10)
Keywords
- Black holes (1)
- Dynamic transport (1)
- Equation of state (1)
- Extra dimensions (1)
- HBT correlation (1)
- LHC (1)
- behavioral performance (1)
- intrinsic plasticity (1)
- local field potential (1)
- prefrontal cortex (1)
Institute
- Physik (10) (remove)
We developed a Monte Carlo event generator for production of nucleon configurations in complex nuclei consistently including effects of nucleon–nucleon (NN) correlations. Our approach is based on the Metropolis search for configurations satisfying essential constraints imposed by short- and long-range NN correlations, guided by the findings of realistic calculations of one- and two-body densities for medium-heavy nuclei. The produced event generator can be used for Monte Carlo (MC) studies of pA and AA collisions. We perform several tests of consistency of the code and comparison with previous models, in the case of high energy proton–nucleus scattering on an event-by-event basis, using nucleus configurations produced by our code and Glauber multiple scattering theory both for the uncorrelated and the correlated configurations; fluctuations of the average number of collisions are shown to be affected considerably by the introduction of NN correlations in the target nucleus. We also use the generator to estimate maximal possible gluon nuclear shadowing in a simple geometric model.
The upcoming high energy experiments at the LHC are one of the most outstanding efforts for a better understanding of nature. It is associated with great hopes in the physics community. But there is also some fear in the public, that the conjectured production of mini black holes might lead to a dangerous chain reaction. In this Letter we summarize the most straightforward arguments that are necessary to rule out such doomsday scenarios.
We apply a coupled transport-hydrodynamics model to discuss the production of multi-strange meta-stable objects in Pb + Pb reactions at the FAIR facility. In addition to making predictions for yields of these particles we are able to calculate particle dependent rapidity and momentum distributions. We argue that the FAIR energy regime is the optimal place to search for multi-strange baryonic object (due to the high baryon density, favoring a distillation of strangeness). Additionally, we show results for strangeness and baryon density fluctuations. Using the UrQMD model we calculate the strangeness separation in phase space which might lead to an enhanced production of MEMOs compared to models that assume global thermalization.
Effects of a phase transition on HBT correlations in an integrated Boltzmann+hydrodynamics approach
(2009)
A systematic study of HBT radii of pions, produced in heavy ion collisions in the intermediate energy regime (SPS), from an integrated (3+1)d Boltzmann+hydrodynamics approach is presented. The calculations in this hybrid approach, incorporating an hydrodynamic stage into the Ultra-relativistic Quantum Molecular Dynamics transport model, allow for a comparison of different equations of state retaining the same initial conditions and final freeze-out. The results are also compared to the pure cascade transport model calculations in the context of the available data. Furthermore, the effect of different treatments of the hydrodynamic freeze-out procedure on the HBT radii are investigated. It is found that the HBT radii are essentially insensitive to the details of the freeze-out prescription as long as the final hadronic interactions in the cascade are taken into account. The HBT radii RL and RO and the RO/RS ratio are sensitive to the EoS that is employed during the hydrodynamic evolution. We conclude that the increased lifetime in case of a phase transition to a QGP (via a Bag Model equation of state) is not supported by the available data.
Superconducting, energy variable heavy ion linac with constant
β, multicell cavities of CH-type
(2009)
An energy variable ion linac consisting of multigap, constant-β cavities was developed. The effect of phase sliding, unavoidable in any constant-β section, is leading to a coherent rf phase motion, which fits well to the H-type structures with their long π-mode sections and separated lenses. The exact periodicity of the cell lengths within each cavity results in technical advantages, such as higher calculation accuracy when only one single period can be simulated, simpler manufacturing, and tuning. This is most important in the case of superconducting cavities. By using this concept, an improved design for a 217 MHz cw superconducting heavy ion linac with energy variation has been worked out. The small output energy spread of ± 3 AkeV is provided over the whole range of energy variation from 3.5 to 7.3 AMeV. These capabilities would allow for a competitive research in the field of radiochemistry and for a production of super heavy elements (SHE), especially. A first 19-cell cavity of that type was designed, built, and rf tested successfully at the Institute for Applied Physics (IAP) Frankfurt. A 325.224 MHz, seven-cell cavity with constant β = 0.16 is under development and will be operated in a frequency controlled mode. It will be equipped with a power coupler and beam tests with Unilac beams at GSI are foreseen.
Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success. Keywords: synaptic plasticity, intrinsic plasticity, recurrent neural networks, reservoir computing, time series prediction
Short-term memory requires the coordination of sub-processes like encoding, retention, retrieval and comparison of stored material to subsequent input. Neuronal oscillations have an inherent time structure, can effectively coordinate synaptic integration of large neuron populations and could therefore organize and integrate distributed sub-processes in time and space. We observed field potential oscillations (14–95 Hz) in ventral prefrontal cortex of monkeys performing a visual memory task. Stimulus-selective and performance-dependent oscillations occurred simultaneously at 65–95 Hz and 14–50 Hz, the latter being phase-locked throughout memory maintenance. We propose that prefrontal oscillatory activity may be instrumental for the dynamical integration of local and global neuronal processes underlying short-term memory.
Den Geheimnissen der Materie auf der Spur : neue Denkfabrik für physikalische Grundlagenforschung
(2009)
Bei Darmstadt entsteht FAIR, eines der größten internationalen Forschungszentren für Physik. Durch das von der Landesregierung geförderte Exzellenzzentrum »HIC for FAIR« erhält die Forschung in Hessen die einmalige Chance, sich direkt an globaler Spitzenforschung zu beteiligen: auf der Suche nach den letzten Geheimnissen der Materie.
Background: In this interdisciplinary project, the biological effects of heavy ions are compared to those of X-rays using tissue slice culture preparations from rodents and humans. Advantages of this biological model are the conservation of an organotypic environment and the independency from genetic immortalization strategies used to generate cell lines. Its open access allows easy treatment and observation via live-imaging microscopy. Materials and methods: Rat brains and human brain tumor tissue are cut into 300 micro m thick tissue slices. These slices are cultivated using a membrane-based culture system and kept in an incubator at 37°C until treatment. The slices are treated with X-rays at the radiation facility of the University Hospital in Frankfurt at doses of up to 40 Gy. The heavy ion irradiations were performed at the UNILAC facility at GSI with different ions of 11.4 A MeV and fluences ranging from 0.5–10 x 106 particles/cm². Using 3D-confocal microscopy, cell-death and immune cell activation of the irradiated slices are analyzed. Planning of the irradiation experiments is done with simulation programs developed at GSI and FIAS. Results: After receiving a single application of either X-rays or heavy ions, slices were kept in culture for up to 9d post irradiation. DNA damage was visualized using gamma H2AXstaining. Here, a dose-dependent increase and time-dependent decrease could clearly be observed for the X-ray irradiation. Slices irradiated with heavy ions showed less gamma H2AX-positive cells distributed evenly throughout the slice, even though particles were calculated to penetrate only 90–100 micro m into the slice. Conclusions: Single irradiations of brain tissue, even at high doses of 40 Gy, will result neither in tissue damage visible on a macroscopic level nor necrosis. This is in line with the view that the brain is highly radio-resistant. However, DNA damage can be detected very well in tissue slices using gamma H2AX-immuno staining. Thus, slice cultures are an excellent tool to study radiation-induced damage and repair mechanisms in living tissues.
Poster presentation: The brain is autonomously active and this self-sustained neural activity is in general modulated, but not driven, by the sensory input data stream [1,2]. Traditionally one has regarded this eigendynamics as resulting from inter-modular recurrent neural activity [3]. Understanding the basic modules for cognitive computation is, in this view, the primary focus of research and the overall neural dynamics would be determined by the the topology of the intermodular pathways. Here we examine an alternative point of view, asking whether certain aspects of the neural eigendynamics have a central functional role for overall cognitive computation [4,5]. Transiently stable neural activity is regularly observed on the cognitive time-scale of 80–100 ms, with indications that neural competition [6] plays an important role in the selection of the transiently stable neural ensembles [7], also denoted winning coalitions [8]. We report on a theory approach which implements these two principles, transient-state dynamics and neural competition, in terms of an associative neural network with clique encoding [9]. A cognitive system [10] with a non-trivial internal eigendynamics has two seemingly contrasting tasks to fulfill. The internal processes need to be regular and not chaotic on one side, but sensitive to the afferent sensory stimuli on the other side. We show, that these two contrasting demands can be reconciled within our approach based on competitive transient-state dynamics, when allowing the sensory stimuli to modulate the competition for the next winning coalition. By testing the system with the bars problem, we find an emerging cognitive capability. Only based on the two basic architectural principles, neural competition and transient-state dynamics, with no explicit algorithmic encoding, the system performs on its own a non-linear independent component analysis of input data stream. The system has rudimentary biological features. All learning is local Hebbian-style, unsupervised and online. It exhibits an ever-ongoing eigendynamics and at no time is the state or the value of synaptic strengths reset or the system restarted; there is no separation between training and performance. We believe that this kind of approach – cognitive computation with autonomously active neural networks – to be an emerging field, relevant both for system neuroscience and synthetic cognitive systems.