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Results for “supernova”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Axion echoes from the supernova graveyard
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Enhanced low-energy supernova burst detection in large liquid argon time projection chambers enabled by Q-Pix
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Diffuse supernova neutrino background as a probe of late-time neutrino mass generation
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Sensitivity to supernovae average ν x temperature with neutral current interactions in DUNE
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Probing self-interacting sterile neutrino dark matter with the diffuse supernova neutrino background
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Effects of annihilation with low-energy neutrinos on high-energy neutrinos from binary neutron star mergers and rare core-collapse supernovae
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Remnant masses from 1 D + core-collapse supernovae simulations: Bimodal neutron star mass distribution and black holes in the low-mass gap
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Two-dimensional models of core-collapse supernova explosions assisted by heavy sterile neutrinos
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Dependence of the reconstructed core-collapse supernova gravitational wave high-frequency feature on the nuclear equation of state in real interferometric data
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Quasithermal GeV neutrinos from neutron-loaded magnetized outflows in core-collapse supernovae: Spectra and light curves
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Gravitational-wave Signature of a First-order Quantum Chromodynamics Phase Transition in Core-Collapse Supernovae
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Muons in Supernovae: Implications for the Axion-Muon Coupling
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Fast neutrino-flavor conversion in the preshock region of core-collapse supernovae
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Sapsan: Framework for Supernovae Turbulence Modeling with Machine Learning
Sapsan is a framework designed to make Machine Learning (ML) more accessible in the study of turbulence, with a focus on astrophysical applications. Sapsan includes modules to load, filter, subsample, batch, and split the data from hydrodynamic (HD) simulations for training and validation. Next, the framework includes built-in conventional and physically-motivated estimators that have been used for turbulence modeling. This ties into Sapsan’s custom estimator module, aimed at designing a custom ML model layer-by-layer, which is the core benefit of using the framework. To share your custom model, every new project created via Sapsan comes with pre-filled, ready-for-release Docker files. Furthermore, training and evaluation modules come with Sapsan as well. The latter, among other features, includes the construction of power spectra and comparison to established analytical turbulence closure models, such as a gradient model. Thus, Sapsan attempts to minimize the hard work required for data preparation and analysis, leaving one to focus on the ML model design itself.
Core-Collapse Supernovae Simulations using FleCSPH
Abstract not provided.