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Register a new userDeep Learning of the Eddington Tensor in the Core-collapse Supernova Simulation
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We trained deep neural networks (DNNs) as a function of the neutrino energy density, flux, and the fluid velocity to reproduce the Eddington tensor for neutrinos obtained in our first-principles core-collapse supernova (CCSN) simulations. Although the moment method, which is one of the most popular approximations for neutrino transport, requires a closure relation, none of the analytical closure relations commonly employed in the literature captures all aspects of the neutrino angular distribution in momentum space. In this paper, we developed a closure relation by using the DNN that takes the neutrino energy density, flux, and the fluid velocity as the input and the Eddington tensor as the output. We consider two kinds of DNNs: a conventional DNN named a component-wise neural network (CWNN) and a tensor-basis neural network (TBNN). We found that the diagonal component of the Eddington tensor is reproduced better by the DNNs than the M1-closure relation especially for low to intermediate energies. For the off-diagonal component, the DNNs agree better with the Boltzmann solver than the M1 closure at large radii. In the comparison between the two DNNs, the TBNN has slightly better performance than the CWNN. With the new closure relations at hand based on the DNNs that well reproduce the Eddington tensor with much smaller costs, we opened up a new possibility for the moment method.
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The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observation run, O2. We trained a newly developed Mini-Inception Resnet neural network using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D numerical simulations of CCSNe. With this algorithm we were able to identify signals from both our phenomenological template bank and from actual numerical 3D simulations of CCSNe. We computed the detection efficiency versus the source distance, obtaining that, for signal to noise ratio higher than 15, the detection efficiency is 70 % at a false alarm rate lower than 5%. We notice also that, in the case of O2 run, it would have been possible to detect signals emitted at 1 kpc of distance, whilst lowering down the efficiency to 60%, the event distance reaches values up to 14 kpc.
One- (1D) and two-dimensional (2D) core-collapse supernova simulations using full Boltzmann neutrino transport for 11.2M and 15.0M progenitor models have been performed to verify the closure relation for the moment method used in the approximate radiation transfer. This study finds areas where the results of the closure relation are inconsistent with those of Boltzmann transport, even for rotational models. In 1D simulations, the Eddington factors p defined in the fluid rest frame (FR) are compared to evaluate the maximum entropy closure for the Fermi-Dirac distribution (MEFD), confirming that MEFD closure performs better than other closures if p < 1/3 and phase space occupancy e > 0.5. In 2D simulations for non-rotating progenitor models, similar results are obtained from the principal-axis analysis of the Eddington tensor kij measured in FR. However, for rotating progenitor models, the principal axes of kij for Boltzmann transport tilt toward oblique directions where matter and neutrinos move relatively fast in azimuthal directions, while the principal axes of kij for MEFD closure are always parallel or perpendicular to the neutrino flux. Thus, the assumption of axisymmetric angular distribution to the flux direction in the closure relation does not hold in the strongly rotating supernova core in the early post-bounce phase. It is also shown that the deviation of the principal axes of kij from the flux direction increases when evaluated in a laboratory frame (LB). The optically thin and thick terms of the pressure tensor in LB negatively impact results in optically thicker and thinner regions, respectively.
The death of massive stars is believed to involve aspheric explosions initiated by the collapse of an iron core. The specifics of how these catastrophic explosions proceed remain uncertain due, in part, to limited observational constraints on various processes that can introduce asymmetries deep inside the star. Here we present near-infrared observations of the young Milky Way supernova remnant Cassiopeia A, descendant of a type IIb core-collapse explosion, and a three-dimensional map of its interior, unshocked ejecta. The remnants interior has a bubble-like morphology that smoothly connects to and helps explain the multi-ringed structures seen in the remnants bright reverse shocked main shell of expanding debris. This internal structure may have originated from turbulent mixing processes that encouraged the development of outwardly expanding plumes of radioactive 56Ni-rich ejecta. If this is true, substantial amounts of its decay product, 56Fe, may still reside in these interior cavities.
The recent discovery that the Fe-K line luminosities and energy centroids observed in nearby SNRs are a strong discriminant of both progenitor type and circumstellar environment has implications for our understanding of supernova progenitor evolution. Using models for the chemical composition of core-collapse supernova ejecta, we model the dynamics and thermal X-ray emission from shocked ejecta and circumstellar material, modeled as an $r^{-2}$ wind, to ages of 3000 years. We compare the X-ray spectra expected from these models to observations made with the Suzaku satellite. We also model the dynamics and X-ray emission from Type Ia progenitor models. We find a clear distinction in Fe-K line energy centroid between core-collapse and Type Ia models. The core-collapse supernova models predict higher Fe-K line centroid energies than the Type Ia models, in agreement with observations. We argue that the higher line centroids are a consequence of the increased densities found in the circumstellar environment created by the expansion of the slow-moving wind from the massive progenitors.
An important result in core-collapse supernova (CCSN) theory is that spherically-symmetric, one-dimensional simulations routinely fail to explode, yet multi-dimensional simulations often explode. Numerical investigations suggest that turbulence eases the condition for explosion, but how is not fully understood. We develop a turbulence model for neutrino-driven convection, and show that this turbulence model reduces the condition for explosions by about 30%, in concordance with multi-dimensional simulations. In addition, we identify which turbulent terms enable explosions. Contrary to prior suggestions, turbulent ram pressure is not the dominant factor in reducing the condition for explosion. Instead, there are many contributing factors, ram pressure being only one of them, but the dominant factor is turbulent dissipation (TD). Primarily, TD provides extra heating, adding significant thermal pressure, and reducing the condition for explosion. The source of this TD power is turbulent kinetic energy, which ultimately derives its energy from the higher potential of an unstable convective profile. Investigating a turbulence model in conjunction with an explosion condition enables insight that is difficult to glean from merely analyzing complex multi-dimensional simulations. An explosion condition presents a clear diagnostic to explain why stars explode, and the turbulence model allows us to explore how turbulence enables explosion. Though we find that turbulent dissipation is a significant contributor to successful supernova explosions, it is important to note that this work is to some extent qualitative. Therefore, we suggest ways to further verify and validate our predictions with multi-dimensional simulations.
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