Related papers: The accuracy of post-processing nucleosynthesis
Observations of type Ia supernovae (SNe Ia) have led to suggestions of multiple progenitor and explosion scenarios. Distinguishing between scenarios and tying specific SNe Ia to individual scenarios however has so far been challenging.…
We implement the Bayesian inference to retrieve energy spectra of all neutrinos from a galactic core-collapse supernova (CCSN). To achieve high statistics and full sensitivity to all flavours of neutrinos, we adopt a combination of several…
Explosive nucleosynthesis is affected by many uncertainties, particularly regarding assumptions and prescriptions adopted during the evolution of the star. Moreover, simple explosion models are often used in the literature, which can…
Machine learning is applied to derive microscopically parameters of the interacting boson model for nuclear spectroscopy. A physics-guided neural network is proposed, which is trained to map the potential energy landscapes that are…
We review some of the uncertainties in calculating nucleosynthetic yields, focusing on the explosion mechanism. Current yield calculations tend to either use a piston, energy injection, or enhancement of neutrino opacities to drive an…
Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there…
We investigate the density distributions of finite nuclei employing a well-designed deep neural network method. We calculate the target nucleon density distributions with Skyrme density functional theories, which are used to train the…
We analyze the nucleosynthesis yields of various Type Ia supernova explosion simulations including pure detonations in sub- Chandrasekhar mass white dwarfs, double detonations and pure helium detonations of sub-Chandrasekhar mass white…
A novel methodology is developed to extract accurate skeletal reaction models for nuclear combustion. Local sensitivities of isotope mass fractions with respect to reaction rates are modeled based on the forced optimally time-dependent…
Faithful energy reconstruction is foundational for precision neutrino experiments like DUNE, but is hindered by uncertainties in our understanding of neutrino--nucleus interactions. Here, we demonstrate that dense neural networks are very…
A method for integrating the chemical equations associated with nuclear combustion at high temperature is presented and extensively checked. Following the idea of E. M\"uller, the feedback between nuclear rates and temperature was taken…
Modern applications of atomic physics, including the determination of frequency standards, and the analysis of astrophysical spectra, require prediction of atomic properties with exquisite accuracy. For complex atomic systems,…
We present optical photometry and spectroscopy of five type Ia supernovae discovered by the Nearby Supernova Factory selected to be spectroscopic analogues of the candidate super-Chandrasekhar-mass events SN 2003fg and SN 2007if. Their…
We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We train the network on a dataset of experimentally measured…
Thermal bombs are a widely used method to artificially trigger explosions of core-collapse supernovae (CCSNe) to determine their nucleosynthesis or ejecta and remnant properties. Recently, their use in spherically symmetric (1D)…
A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…
Machine learning offers a powerful framework for validating and predicting atomic mass. We compare three improved neural network methods for representation and extrapolation for atomic mass prediction. The powerful method, adopting a…
Core-collapse supernovae (CCSNe) are the extremely energetic deaths of massive stars. They play a vital role in the synthesis and dissemination of many heavy elements in the universe. In the past, CCSN nucleosynthesis calculations have…
We present the first calculations to follow the evolution of all stable nuclei and their radioactive progenitors in stellar models computed from the onset of central hydrogen burning through explosion as Type II supernovae. Calculations are…
We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider.…