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Register a new userBayesian evaluation of residual production cross sections in proton induced spallation reactions
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The Bayesian neural network (BNN) method is used to construct a predictive model for fragment prediction of proton induced spallation reactions with the guidance of a simplified EPAX formula. Compared to the experimental data, it is found that the BNN + sEPAX model can reasonably extrapolate with less information compared with BNN method. The BNN + sEPAX method provides a new approach to predict the energy-dependent residual cross sections produced in proton-induced spallation reactions from tens of MeV/u up to several GeV/u.
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Fragments productions in spallation reactions are key infrastructure data for various applications. Based on the empirical parameterizations {sc spacs}, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in the proton induced spallation reactions. A systematic investigation have been performed for the measured proton induced spallation reactions of systems ranging from the intermediate to the heavy nuclei and the incident energy ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and the {sc spacs} predictions, the BNN predicted results are in good agreement with the measured results. The established method is suggested to benefit the related researches in the nuclear astrophysics, nuclear radioactive beam source, accelerator driven systems, and proton therapy, etc.
We studied the complete dynamics of the proton-induced spallation process with the microscopic framework of the Constrained Molecular Dynamics (CoMD) Model. We performed calculations of proton-induced spallation reactions on 181Ta, 208Pb, and 238U targets with the CoMD model and compared the results with a standard two-step approach based on an intranuclear cascade model (INC) followed by a statistical deexcitation model. The calculations were also compared with recent experimental data from the literature. Our calculations showed an overall satisfactory agreement with the experimental data and suggest further improvements in the models. We point out that this CoMD study represents the first complete dynamical description of spallation reactions with a microscopic N-body approach and may lead to advancements in the physics-based modelling of the spallation process.
114 cross sections for nuclide production in a 1.0 GeV proton-irradiated thin 208Pb target have been measured by the direct gamma spectrometry method using a high-resolution Ge detector. The gamma spectra were processed by the GENIE-2000 code. The ITEP-developed SIGMA code was used together with the PCNUDAT nuclear decay database to identify the gamma lines and to determine the cross sections. The 27Al(p,x)22Na reaction was used to monitor the proton flux. Results of a feasibility study of the auxiliary 27Al(p,x)24Na and 27Al(p,x)7Be monitor reactions in the 0.07-2.6 GeV proton-energy range are presented as well. Most of the experimental data have been analyzed by the LAHET (with ISABEL and Bertini options), CEM95, CEM2k, INUCL, CASCADE, CASCADE/INPE, and YIELDX codes that simulate hadron-nucleus interactions.
Spallation residues produced in 1 GeV per nucleon $^{208}$Pb on proton reactions have been studied using the FRagment Separator facility at GSI. Isotopic produc- tion cross-sections of elements from $_{61}$Pm to $_{82}$Pb have been measured down to 0.1 mb with a high accuracy. The recoil kinetic energies of the produced fragments were also determined. The obtained cross-sections agree with most of the few existing gamma-spectroscopy data. Data are compared with different intra nuclear-cascade and evaporation-fission models. Drastic deviations were found for a standard code used in technical applications.
In nuclear reactions induced by hadrons and ions of high energies, nuclei can disintegrate into many fragments during a short time (~100 fm/c). This phenomenon known as nuclear multifragmentation was under intensive investigation last 20 years. It was established that multifragmentation is an universal process taking place in all reactions when the excitation energy transferred to nuclei is high enough, more than 3 MeV per nucleon, independently on the initial dynamical stage of the reactions. Very known compound nucleus decay processes (sequential evaporation and fission), which are usual for low energies, disappear and multifragmentation dominates at high excitation energy. For this reason, calculation of multifragmentation must be carried on in all cases when production of highly excited nuclei is expected, including spallation reactions. From the other hand, one can consider multifragmentation as manifestation of the liquid-gas phase transition in finite nuclei. This gives way for studying nuclear matter at subnuclear densities and for applications of properties of nuclear matter extracted from multifragmentation reactions in astrophysics. In this contribution, the Statistical Multifragmentation Model (SMM), which combines the compound nucleus processes at low energies and multifragmentation at high energies, is described. The most important ingredients of the model are discussed.
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