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We present numerical results from a parameter study of the standing accretion shock instability (SASI), investigating the impact of general relativity (GR) on the dynamics. Using GR hydrodynamics with GR gravity, and nonrelativistic (NR) hydrodynamics with Newtonian gravity, in an idealized model setting, we vary the initial radius of the shock, and by varying its mass and radius in concert, the proto-neutron star compactness. We investigate four compactnesses expected in a post-bounce core-collapse supernova (CCSN). We find that GR leads to a longer SASI oscillation period, with ratios between the GR and NR cases as large as 1.29 for the highest- compactness suite. We also find that GR leads to a slower SASI growth rate, with ratios between the GR and NR cases as low as 0.47 for the highest-compactness suite. We discuss implications of our results for CCSN simulations. 

We present a methodology based on the implementation of a fully connected neural network algorithm to estimate the temporal evolution of the high-frequency gravitational wave emission for a core collapse supernova (CCSN). For this study, we selected a fully connected deep neural network (DNN) regression model because it can learn both linear and nonlinear relationships between the input and output data, it is more appropriate for handling large-dimensional input data, and it offers high performance at a low computational cost. To train the Machine Learning (ML) algorithm, we construct a training dataset using synthetic waveforms, and several CCSN waveforms are used to test the algorithm. We performed a first-order estimation of the high-frequency gravitational wave emission on real interferometric LIGO data from the second half of the third observing run (O3b) with a two detector network (L1 and H1). The relative error associated with the estimate of the slope of the resonant frequency versus time for the GW from CCSN signals is within 13% for the tested candidates included in this study up to different Galactic distances (1.0, 2.3, 3.1, 4.3, 5.4, 7.3, and 10 kpc). This method is, to date, the best estimate of the temporal evolution of the high-frequency emission in real interferometric data. Our methodology of estimation can be used in future studies focused on physical properties of the progenitor. The distances where comparable performances could be achieved for Einstein Telescope and Cosmic Explorer roughly rescale with the noise floor improvements. 

We compare the core-collapse evolution of a pair of 15.8 M☉ stars with significantly different internal structures, a consequence of the bimodal variability exhibited by massive stars during their late evolutionary stages. The 15.78 and 15.79 M☉ progenitors have core masses (masses interior to an entropy of 4 kB baryon−1) of 1.47 and 1.78 M☉ and compactness parameters ξ1.75 of 0.302 and 0.604, respectively. The core-collapse simulations are carried out in 2D to nearly 3 s postbounce and show substantial differences in the times of shock revival and explosion energies. The 15.78 M☉ model begins exploding promptly at 120 ms postbounce when a strong density decrement at the Si– Si/O shell interface, not present in the 15.79 M☉ progenitor, encounters the stalled shock. The 15.79 M☉ model takes 100 ms longer to explode but ultimately produces a more powerful explosion. Both the larger mass accretion rate and the more massive core of the 15.79 M☉ model during the first 0.8 s postbounce time result in larger νe/n ̄e luminosities and RMS energies along with a flatter and higher-density heating region. The more-energetic explosion of the 15.79 M☉ model resulted in the ejection of twice as much 56Ni. Most of the ejecta in both models are moderately proton rich, though counterintuitively the highest electron fraction (Ye = 0.61) ejecta in either model are in the less-energetic 15.78 M☉ model, while the lowest electron fraction (Ye = 0.45) ejecta in either model are in the 15.79 M☉ model. 

Abstract Motivated by their role as the direct or indirect source of many of the elements in the Universe, numerical modeling of core collapse supernovae began more than five decades ago. Progress toward ascertaining the explosion mechanism(s) has been realized through increasingly sophisticated models, as physics and dimensionality have been added, as physics and numerical modeling have improved, and as the leading computational resources available to modelers have become far more capable. The past five to ten years have witnessed the emergence of a consensus across the core collapse supernova modeling community that had not existed in the four decades prior. For the majority of progenitors – i.e., slowly rotating progenitors – the efficacy of the delayed shock mechanism, where the stalled supernova shock wave is revived by neutrino heating by neutrinos emanating from the proto-neutron star, has been demonstrated by all core collapse supernova modeling groups, across progenitor mass and metallicity. With this momentum, and now with a far deeper understanding of the dynamics of these events, the path forward is clear. While much progress has been made, much work remains to be done, but at this time we have every reason to be optimistic we are on track to answer one of the most important outstanding questions in astrophysics: How do massive stars end their lives? 

Abstract The proposal that core collapse supernovae are neutrino driven is still the subject of active investigation more than 50 years after the seminal paper by Colgate and White. The modern version of this paradigm, which we owe to Wilson, proposes that the supernova shock wave is powered by neutrino heating, mediated by the absorption of electron-flavor neutrinos and antineutrinos emanating from the proto-neutron star surface, or neutrinosphere. Neutrino weak interactions with the stellar core fluid, the theory of which is still evolving, are flavor and energy dependent. The associated neutrino mean free paths extend over many orders of magnitude and are never always small relative to the stellar core radius. Thus, neutrinos are never always fluid like. Instead, a kinetic description of them in terms of distribution functions that determine the number density of neutrinos in the six-dimensional phase space of position, direction, and energy, for both neutrinos and antineutrinos of each flavor, or in terms of angular moments of these neutrino distributions that instead provide neutrino number densities in the four-dimensional phase-space subspace of position and energy, is needed. In turn, the computational challenge is twofold: (i) to map the kinetic equations governing the evolution of these distributions or moments onto discrete representations that are stable, accurate, and, perhaps most important, respect physical laws such as conservation of lepton number and energy and the Fermi–Dirac nature of neutrinos and (ii) to develop efficient, supercomputer-architecture-aware solution methods for the resultant nonlinear algebraic equations. In this review, we present the current state of the art in attempts to meet this challenge.