Hydrogen Burning of 17-O in Classical Novae

We report on the observation of a previously unknown resonance at E=194.1+/-0.6 keV (lab) in the 17-O(p,alpha)14-N reaction, with a measured resonance strength omega_gamma(p,alpha)=1.6+/-0.2 meV. We studied in the same experiment the 17-O(p,gamma)18-F reaction by an activation method and the resonance-strength ratio was found to be omega_gamma(p,alpha)/omega_gamma(p,gamma)=470+/-50. The corresponding excitation energy in the 18-F compound nucleus was determined to be 5789.8+/-0.3 keV by gamma-ray measurements using the 14-N(alpha,gamma)18-F reaction. These new resonance properties have important consequences for 17-O nucleosynthesis and gamma-ray astronomy of classical novae.Comment: 4 pages, 4 figures. Accepted for publication in Physical Review Letter

Gamma ray production cross sections in proton induced reactions on natural Mg, Si and Fe targets over the proton energy range 30 up to 66 MeV

Gamma-ray excitation functions have been measured for 30, 42, 54 and 66 MeV proton beams accelerated onto C + O (Mylar), Mg, Si, and Fe targets of astrophysical interest at the separate-sector cyclotron of iThemba LABS in Somerset West (Cape Town, South Africa). A large solid angle, high energy resolution detection system of the Eurogam type was used to record Gamma-ray energy spectra. Derived preliminary results of Gamma-ray line production cross sections for the Mg, Si and Fe target nuclei are reported and discussed. The current cross section data for known, intense Gamma-ray lines from these nuclei consistently extend to higher proton energies previous experimental data measured up to Ep ~ 25 MeV at the Orsay and Washington tandem accelerators. Data for new Gamma-ray lines observed for the first time in this work are also reported.Comment: 11 pages, 6 figures. IOP Institute of Physics Conference Nuclear Physics in Astrophysics VII, 28th EPF Nuclear Physics Divisional Conference, May 18-22 2015, York, U

Indirect study of 19Ne states near the 18F+p threshold

The early E < 511 keV gamma-ray emission from novae depends critically on the 18F(p,a)15O reaction. Unfortunately the reaction rate of the 18F(p,a)15O reaction is still largely uncertain due to the unknown strengths of low-lying proton resonances near the 18F+p threshold which play an important role in the nova temperature regime. We report here our last results concerning the study of the d(18F,p)19F(alpha)15N transfer reaction. We show in particular that these two low-lying resonances cannot be neglected. These results are then used to perform a careful study of the remaining uncertainties associated to the 18F(p,a)15O and 18F(p,g)19Ne reaction rates.Comment: 18 pages, 8 figures. Accepted in Nuclear Physics

Detectability of gamma-ray emission from classical novae with Swift/BAT

Classical novae are expected to emit gamma rays during their explosions. The most important contribution to the early gamma-ray emission comes from the annihilation with electrons of the positrons generated by the decay of 13N and 18F. The photons are expected to be down-scattered to a few tens of keV, and the emission is predicted to occur some days before the visual discovery and to last ~2 days. Despite a number of attempts, no positive detections of such emission have been made, due to lack of sensitivity and of sky coverage. Because of its huge field of view, good sensitivity, and well-adapted energy band, Swift/BAT offers a new opportunity for such searches. BAT data can be retrospectively used to search for prompt gamma-ray emission from the direction of novae after their optical discovery. We have estimated the expected success rate for the detection with BAT of gamma rays from classical novae using a Monte Carlo approach. Searches were performed for emission from novae occurring since the launch of Swift. Using the actual observing program during the first 2.3 years of BAT operations as an example, and sensitivity achieved, we estimate the expected rate of detection of classical novae with BAT as ~0.2-0.5/yr, implying that several should be seen within a 10 yr mission. The search for emission in the directions of the 24 classical novae discovered since the Swift launch yielded no positive results, but none of these was known to be close enough for this to be a surprise. Detections of a recurrent nova (RS Oph) and a nearby dwarf nova (V455 And) demonstrate the efficacy of the technique. The absence of detections is consistent with the expectations from the Monte Carlo simulations, but the long-term prospects are encouraging given an anticipated Swift operating lifetime of ~10 years.Comment: 10 pages, 6 figures. Accepted for publication in A&

Measurement and analysis of nuclear γ-ray production cross sections in proton interactions with Mg, Si, and Fe nuclei abundant in astrophysical sites over the incident energy range E = 30–66 MeV

The modeling of nuclear γ -ray line emission induced by highly accelerated particles in astrophysical sites (e.g., solar flares, the gas and dust in the inner galaxy) and the comparison with observed emissions from these sites needs a comprehensive database of related production cross sections. The most important reactions of protons and α particles are those with abundant target elements like C, O, N, Ne, Mg, Si, and Fe at projectile energies extending from the reaction threshold to a few hundred MeV per nucleon. In this work, we have measured γ -ray production cross section excitation functions for 30, 42, 54, and 66 MeV proton beams accelerated onto nat C , C + O (Mylar), nat Mg , nat Si , and 56 Fe targets of astrophysical interest at the Separated Sector Cyclotron (SSC) of iThemba LABS (near Cape Town, South Africa). The AFRODITE array equipped with eight Compton suppressed high-purity (HPGe) clover detectors was used to record γ -ray line energy spectra. For known, intense lines previously reported experimental data measured up to E p ≃ 25 MeV at the Washington and Orsay tandem accelerators were thus extended to higher proton energies. Our experimental data for the last three targets are reported here and discussed with respect to previous data and to the Murphy et al. compilation [Astrophys. J. Suppl. Ser. 183, 142 (2009)]

Neurocomputing Techniques to Predict the 2D Structures by Using Lattice Dynamics of Surfaces

A theoretical study of artificial neural network modelling, based on vibrational dynamic data for 2D lattice, is proposed in this paper. The main purpose is to establish a neurocomputing model able to predict the 2D structures of crystal surfaces. In material surfaces, atoms can be arranged in different possibilities, defining several 2D configurations, such as triangular, square lattices, etc. To describe these structures, we usually employ the Wood notations, which are considered as the simplest manner and the most frequently used to spot the surfaces in physics. Our contribution consists to use the vibration lattice of perfect 2D structures along with the matrix and Wood notations to build up an input-output set to feed the neural model. The input data are given by the frequency modes over high symmetry points and the group velocity. The output data are given by the basis vectors corresponding to surface reconstruction and the rotation angle which aligns the unit cell of the reconstructed surface. Results showed that the method of collecting the dataset was very suitable for building a neurocomputing model that is able to predict and classify the 2D surface of the crystals. Moreover, the model was able to generate the lattice spacing for a given structure

Comparison of Second Order Algorithms for Function Approximation with Neural Networks

The Neural networks are massively parallel, distributed processing systems representing a new computational technology built on the analogy to the human information processing system. They are usually considered as naturally parallel computing models. The combination of wavelets with neural networks can hopefully remedy each other's weaknesses, resulting in wavelet based neural network capable of approximating any function with arbitrary precision. A wavelet based neural network is a nonlinear regression structure that represents nonlinear mappings as the superposition of dilated and translated versions of a function, which is found both in the space and frequency domains. The desired task is usually obtained by a learning procedure which consists in adjusting the "synaptic weights". For this purpose, many learning algorithms have been proposed to update these weights. The convergence for these learning algorithms is a crucial criterion for neural networks to be useful in different applications. In this paper, we use different training algorithms for feed forward wavelet networks used for function approximation. The training is based on the minimization of the least-square cost function. The minimization is performed by iterative first and second order gradient-based methods. We make use of the Levenberg-Marquardt algorithm to train the architecture of the chosen network and, then, the training procedure starts with a simple gradient method which is followed by a BFGS (Broyden, Fletcher, Glodfarb et Shanno) algorithm. The conjugate gradient method is then used. The performances of the different algorithms are then compared. It is found that the advantage of the last training algorithm, namely, conjugate gradient method, over many of the other optimization algorithms is its relative simplicity, efficiency and quick convergence

Continuous Functions Modeling with Artificial Neural Network: An Improvement Technique to Feed the Input-Output Mapping

The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage

Effect of Temperature and PH on Biogas Production from Cow Dung and Dog Faeces

The effect of feed, temperature and pH on biogas production was investigated using 500 ml small scale laboratory flasks. Feed containing cow dung and dog faeces produced the most biogas for small scale experiments. The combinations were scaled up to assess the feasibility of producing biogas from two 150 L bio-digester containers in natural conditions, with the containers being placed above and below ground. Variation of temperature in the mesophilic range did not have much effect on the concentration of methane produced. High temperatures favoured more methane production compared to low temperatures. The maximum concentration of methane was 45 per cent in the laboratory experiment at 30oC and 34 per cent in the digester above the ground. The low pH values may have inhibited the action of the methanogenic bacteria, resulting in low methane production in samples with food waste. A total biogas volume of 31.8 L was produced per 50 kg mass of feed in the above-ground bio-digester after 66 days