相关论文: The accuracy of post-processing nucleosynthesis
Nucleosynthesis is a complex process in astro-nuclear evolution. In this work, we construct a directed multi-layer nuclear reaction network using the substrate-product method from a thermonuclear reaction database, JINA REACLIB. The network…
After training complex deep learning models, a common task is to compress the model to reduce compute and storage demands. When compressing, it is desirable to preserve the original model's per-example decisions (e.g., to go beyond top-1…
Resolving the small length-scale of thermonuclear detonation waves (TNDWs) in supernovae is currently not possible in multidimensional full-star simulations. Additionally, multidimensional simulations usually use small, oversimplistic…
During the first few hundred days after the explosion, core-collapse supernovae (SNe) emit down-scattered X-rays and gamma-rays originating from radioactive line emissions, primarily from the $^{56}$Ni $\rightarrow$ $^{56}$Co $\rightarrow$…
We demonstrate the use of neural networks to accelerate the reaction steps in the MAESTROeX stellar hydrodynamics code. A traditional MAESTROeX simulation uses a stiff ODE integrator for the reactions; here we employ a ResNet architecture…
Measurements of explosive nucleosynthesis yields in core-collapse supernovae provide tests for explosion models. We investigate constraints on explosive conditions derivable from measured amounts of nickel and iron after radioactive decays…
This review concentrates on nucleosynthesis processes in general and their applications to massive stars and supernovae. A brief initial introduction is given to the physics in astrophysical plasmas which governs composition changes. We…
Background: In the environment of high neutrino-fluxes provided in core-collapse supernovae or neutron star mergers, neutrino-induced reactions with nuclei contribute to the nucleosynthesis processes. A number of terrestrial neutrino…
Simulations of nucleosynthesis in astrophysical environments are at the intersection of nuclear physics reaction rate research and astrophysical applications, for example in the area of galactic chemical evolution or near-field cosmology.…
Spiking Neural Networks (SNNs) are increasingly studied as energy-efficient alternatives to Convolutional Neural Networks (CNNs), particularly for edge intelligence. However, prior work has largely emphasized large-scale models, leaving the…
The interpretation of supernova (SN) spectra is essential for deriving SN ejecta properties such as density and composition, which in turn can tell us about their progenitors and the explosion mechanism. A very large number of atomic…
A non-local-thermodynamic-equilibrium (NLTE) level population model of the first and second ionisation stages of iron, nickel and cobalt is used to fit a sample of XShooter optical + near-infrared (NIR) spectra of Type Ia supernovae (SNe…
We study in detail the ejecta conditions and theoretical nucleosynthetic results for 18 three-dimensional core-collapse supernova (CCSN) simulations done by F{\sc ornax}. {Most simulations are carried out to at least 3 seconds after bounce,…
Predictive coding networks are neural models that perform inference through an iterative energy minimization process, whose operations are local in space and time. While effective in shallow architectures, they suffer significant…
Nuclear yields are powerful probes of supernova explosions, their engines and their progenitors. In addition, as we improve our understanding of these explosions, we can use nuclear yields to probe dense matter and neutrino physics, both of…
We calculate explosive nucleosynthesis in Chandrasekhar mass models for Type Ia Supernovae(SNe Ia) to obtain new constraints on the rate of matter accretion onto the progenitor white dwarf and on the ignition density of central carbon…
In Type Ia Supernovae (\sneia), the relative abundances of chemical elements are affected by the neutron excess in the composition of the progenitor white dwarf. Since these products leave signatures in the spectra near maximum light,…
Infinite nuclear matter provides valuable insights into the behavior of nuclear systems and aids our understanding of atomic nuclei and large-scale stellar objects such as neutron stars. However, partly due to the large basis needed to…
We investigate the post-explosion phase in core-collapse supernovae with 2D hydrodynamical simulations and a simple neutrino treatment. The latter allows us to perform 46 simulations and follow the evolution of the 32 successful explosions…
We illustrate methods for deriving properties of thermonuclear, or Type Ia, supernovae, including synthesized $^{56}$Ni mass, total ejecta mass, ejecta kinetic energy, and $^{56}$Ni distribution in velocity, from gamma-ray line…