Related papers: The accuracy of post-processing nucleosynthesis
Our work aims to reproduce the set of findings published in "Network Deconvolution" by Ye et al. (2020)[1]. That paper proposes an optimization technique for model training in convolutional neural networks. The proposed technique "network…
This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an…
We investigate whether pure deflagration models of Chandrasekhar-mass carbon-oxygen white dwarf stars can account for one or more subclass of the observed population of Type Ia supernova (SN Ia) explosions. We compute a set of 3D full-star…
We present new nucleosynthesis yields as functions of the stellar mass, metallicity, and explosion energy (corresponding to normal supernovae and Hypernovae). We apply the results to the chemical evolution of the solar neighborhood. Our new…
Theoretical modeling of nucleus-nucleus collision often is based on the nucleus-nucleus potential. One of the advanced methods for constructing this potential is the semi-microscopical double-folding model with the M3Y-Paris NN-forces.…
(Abridged) Modern studies and industrial applications related to the design, radiation protection, and reliability of nuclear facilities, medical applications, as well as space research and exploration are relying on extensive simulations…
Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images. Deep learning algorithms have shown strong potential in nuclei detection tasks. However, they face challenges when applied to pathology images…
Type Ia supernovae (SNIa) are thermonuclear explosions of white dwarfs in binary systems. They are central to galactic chemical evolution and serve as standardizable candles in cosmology, yet their progenitors remain uncertain. In this…
We provide yields from 189 neutrino-driven core-collapse supernova (CCSN) simulations covering zero-age main sequence masses between 11 and 75 solar masses and three different metallicities. Our CCSN simulations have two main advantages…
In the new era of time-domain surveys Type Ia supernovae are being caught sooner after explosion, which has exposed significant variation in their early light curves. Two driving factors for early time evolution are the distribution of…
In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime.…
In order to study the processes creating intermediate and heavy nuclei in massive stars it is necessary to provide neutron capture cross sections and reaction rates close to stability and for moderately unstable neutron-rich nuclei.…
There are two classes of viable progenitors for normal Type Ia supernovae (SNe Ia): systems in which a white dwarf explodes at the Chandrasekhar mass ($M_{ch}$), and systems in which a white dwarf explodes below the Chandrasekhar mass…
Large-eddy simulations (LES) require closures for filtered production rates because the resolved fields do not contain all correlations that govern chemical source terms. We develop a graph neural network (GNN) that predicts filtered…
The delayed-detonation explosion mechanism applied to a Chandrasekhar-mass white dwarf offers a very attractive model to explain the inferred characteristics of Type Ia supernovae (SNe Ia). The resulting ejecta are chemically stratified,…
We present results for a suite of fourteen three-dimensional, high resolution hydrodynamical simulations of delayed-detonation modelsof Type Ia supernova (SN Ia) explosions. This model suite comprises the first set of three-dimensional SN…
In the first part of this paper, we propose new optimization-based methods for the computation of preferred (dense, sparse, reversible, detailed and complex balanced) linearly conjugate reaction network structures with mass action dynamics.…
Energetics of nuclear reaction is fundamentally important to understand the mechanism of pair instability supernovae (PISNe). Based on the hydrodynamic equations and thermodynamic relations, we derive exact expressions for energy…
Deep networks, especially convolutional neural networks (CNNs), have been successfully applied in various areas of machine learning as well as to challenging problems in other scientific and engineering fields. This paper introduces…
We perform some experimental simulations in spherical symmetry and axisymmetry to understand the post-shock-revival evolution of core-collapse supernovae. Assuming that the stalled shock wave is relaunched by neutrino heating and employing…