Related papers: The accuracy of post-processing nucleosynthesis
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data-driven. However, neural networks (NNs) often lead…
Ternary fission yields in the reaction 241Pu(nth,f) are calculated using a new model which assumes a nucleation-time moderated chemical equilibrium in the low density matter which constitutes the neck region of the scissioning system. The…
Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. However, prevailing energy evaluations often oversimplify,…
Theoretical and observational approaches to settling the important questions surrounding the progenitor systems and the explosion mechanism of normal Type Ia supernovae have thus far failed. With its unique capability to obtain continuous…
113 residual product nuclide yields in a 1.0 GeV proton-irradiated thin monoisotopic 208-Pb sample and 107 residual product nuclide yields in a 2.6 GeV proton-irradiated nat-W sample have been measured and simulated by 8 different codes.…
Magnetorotational supernovae are a rare type of core-collapse supernovae where the magnetic field and rotation play a central role in the dynamics of the explosion. We present the post-processed nucleosynthesis of state-of-the-art…
Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods…
A Kohn-Sham scheme based multi-task neural network is elaborated for the supervised learning of nuclear shell evolution. The training set is composed of the single-particle wave functions and occupation probabilities of 320 nuclei,…
Type Ia supernova explosions (SNIa) are fundamental sources of elements for the chemical evolution of galaxies. They efficiently produce intermediate-mass (with Z between 11 and 20) and iron group elements - for example, about 70% of the…
A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amidst a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results…
Type-Ia supernovae (SN Ia) are powerful stellar explosions that provide important distance indicators in cosmology. Recently, we proposed a new SN Ia mechanism that involves a nuclear fission chain-reaction in an isolated white dwarf [PRL…
We present a new approach to understand the landscape of supernova explosion energies, ejected nickel masses, and neutron star birth masses. In contrast to other recent parametric approaches, our model predicts the properties of…
Core-collapse supernovae (CCSNe) are expected to produce intense bursts of neutrinos preceding the emergence of their electromagnetic (EM) counterparts. The prompt detection of such neutrino signals offers a unique opportunity to trigger…
Spiking Neural Networks (SNN) are more closely related to brain-like computation and inspire hardware implementation. This is enabled by small networks that give high performance on standard classification problems. In literature, typical…
We perform an extensive study of the influence of nuclear weak interactions on core-collapse supernovae (CCSNe), paying particular attention to consistency between nuclear abundances in the equation of state (EOS) and nuclear weak…
We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of ${\cal O}(N^6)$ in time and ${\cal O}(N^4)$ in storage. The description of the…
Multidimensional nucleosynthesis studies with hundreds of nuclei linked through thousands of nuclear processes are still computationally prohibitive. To date, most nucleosynthesis studies rely either on hydrostatic/hydrodynamic simulations…
Computing high-dimensional potential energy surfaces for molecular systems and materials is considered to be a great challenge in computational chemistry with potential impact in a range of areas including the fundamental prediction of…
There is renewed interest in developing small modular reactors and micro-reactors. Innovation is necessary in both construction and operation methods of these reactors to be financially attractive. For operation, an area of interest is the…
Neutron star mergers are today considered a major production site for rapid neutron capture elements. While the bulk of the matter escapes at fast, but non-relativistic velocities (${\sim} 0.2\,c$), a small amount of the dynamically ejected…