Search for: All records

Abstract In order to better connect core-collapse supernova (CCSN) theory with its observational signatures, we have developed a simulation pipeline from the onset of the core collapse to beyond shock breakout from the stellar envelope. Using this framework, we present a 3D simulation study from 5 s to over 5 days following the evolution of a 17M_⊙progenitor, exploding with ∼10⁵¹erg of energy and ∼0.1M_⊙of⁵⁶Ni ejecta. The early explosion is highly asymmetric, expanding most prominently along the southern hemisphere. This early asymmetry is preserved to shock breakout, ∼1 day later. Breakout itself evinces strong angle-dependence, with as much as 1 day delay in the shock breakout by direction. The nickel ejecta closely tail the forward shock, with velocities at the breakout as high as ∼7000 km s⁻¹. A delayed reverse shock forming at the H/He interface on hour timescales leads to the formation of Rayleigh–Taylor instabilities, fast-moving nickel bullets, and almost complete mixing of the metal core into the hydrogen envelope. For the first time, we illustrate the angle-dependent emergent broadband and bolometric light curves from simulations evolved in 3D in entirety, continuing through hydrodynamic shock breakout from a CCSN model of a massive stellar progenitor evolved with detailed, late-time neutrino microphysics and transport. Our case study of a single progenitor underscores that 3D simulations generically produce the cornucopia of observed asymmetries and features in CCSNe observations, while establishing the methodology to study this problem in breadth. 

Abstract We analyze the directional dependence of the gravitational wave (GW) emission from 15 3D neutrino radiation hydrodynamic simulations of core-collapse supernovae (CCSNe). Using spin weighted spherical harmonics, we develop a new analytic technique to quantify the evolution of the distribution of GW emission over all angles. We construct a physics-informed toy model that can be used to approximate GW distributions for general ellipsoid-like systems, and use it to provide closed form expressions for the distribution of GWs for different CCSN phases. Using these toy models, we approximate the protoneutron star (PNS) dynamics during multiple CCSN stages and obtain similar GW distributions to simulation outputs. When considering all viewing angles, we apply this new technique to quantify the evolution of preferred directions of GW emission. For nonrotating cases, this dominant viewing angle drifts isotropically throughout the supernova, set by the dynamical timescale of the PNS. For rotating cases, during core bounce and the following tens of milliseconds, the strongest GW signal is observed along the equator. During the accretion phase, comparable—if not stronger—GW amplitudes are generated along the axis of rotation, which can be enhanced by the lowT/∣W∣ instability. We show two dominant factors influencing the directionality of GW emission are the degree of initial rotation and explosion morphology. Lastly, looking forward, we note the sensitive interplay between GW detector site and supernova orientation, along with its effect on detecting individual polarization modes. 

Abstract Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine-learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with F ornax , we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9−27 M ⊙ , we additionally train an autoencoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and autoencoder features predict the explosion outcome with ≈90% accuracy. In anticipation of much larger multidimensional simulation sets, we identify future directions in which machine-learning applications will be useful beyond the explosion outcome prediction. 

Abstract Calibrating with detailed 2D core-collapse supernova (CCSN) simulations, we derive a simple CCSN explosion condition based solely upon the terminal density profiles of state-of-the-art stellar evolution calculations of the progenitor massive stars. This condition captures the vast majority of the behaviour of the one hundred 2D state-of-the-art models we performed to gauge its usefulness. The goal is to predict, without resort to detailed simulation, the explodability of a given massive star. We find that the simple maximum fractional ram pressure jump discriminant we define works well ∼90 per cent of the time and we speculate on the origin of the few false positives and false negatives we witness. The maximum ram pressure jump generally occurs at the time of accretion of the silicon/oxygen interface, but not always. Our results depend upon the fidelity with which the current implementation of our code F ornax adheres to Nature and issues concerning the neutrino–matter interaction, the nuclear equation of state, the possible effects of neutrino oscillations, grid resolution, the possible role of rotation and magnetic fields, and the accuracy of the numerical algorithms employed remain to be resolved. Nevertheless, the explodability condition we obtain is simple to implement, shows promise that it might be further generalized while still employing data from only the unstable Chandrasekhar progenitors, and is a more credible and robust simple explosion predictor than can currently be found in the literature. 

ABSTRACT We study theoretical neutrino signals from core-collapse supernova (CCSN) computed using axisymmetric CCSN simulations that cover the post-bounce phase up to ∼4 s. We provide basic quantities of the neutrino signals such as event rates, energy spectra, and cumulative number of events at some terrestrial neutrino detectors, and then discuss some new features in the late phase that emerge in our models. Contrary to popular belief, neutrino emissions in the late phase are not always steady, but rather have temporal fluctuations, the vigour of which hinges on the CCSN model and neutrino flavour. We find that such temporal variations are not primarily driven by proto-neutron star convection, but by fallback accretion in exploding models. We assess the detectability of these temporal variations, and find that IceCube is the most promising detector with which to resolve them. We also update fitting formulae first proposed in our previous paper for which the total neutrino energy emitted at the CCSN source is estimated from the cumulative number of events in each detector. This will be a powerful technique with which to analyse real observations, particularly for low-statistics data. 

ABSTRACT Based on our recent three-dimensional core-collapse supernova (CCSN) simulations including both exploding and non-exploding models, we study the detailed neutrino signals in representative terrestrial neutrino observatories, namely Super-Kamiokande (Hyper-Kamiokande), DUNE, JUNO, and IceCube. We find that the physical origin of difference in the neutrino signals between 1D and 3D is mainly proto-neutron-star convection. We study the temporal and angular variations of the neutrino signals and discuss the detectability of the time variations driven by the spiral standing accretion shock instability (spiral SASI) when it emerges for non-exploding models. In addition, we determine that there can be a large angular asymmetry in the event rate ($${\gtrsim} 50 {{\ \rm per\ cent}}$$), but the time-integrated signal has a relatively modest asymmetry ($${\lesssim} 20 {{\ \rm per\ cent}}$$). Both features are associated with the lepton-number emission self-sustained asymmetry and the spiral SASI. Moreover, our analysis suggests that there is an interesting correlation between the total neutrino energy (TONE) and the cumulative number of neutrino events in each detector, a correlation that can facilitate data analyses of real observations. We demonstrate the retrieval of neutrino energy spectra for all flavours of neutrino by applying a novel spectrum reconstruction technique to the data from multiple detectors. We find that this new method is capable of estimating the TONE within the error of ∼20 per cent if the distance to the CCSN is ≲6 kpc. 

ABSTRACT Using our new state-of-the-art core-collapse supernova (CCSN) code Fornax, we explore the dependence upon spatial resolution of the outcome and character of three-dimensional (3D) supernova simulations. For the same 19 M⊙ progenitor star, energy and radial binning, neutrino microphysics, and nuclear equation of state, changing only the number of angular bins in the θ and ϕ directions, we witness that our lowest resolution 3D simulation does not explode. However, when jumping progressively up in resolution by factors of two in each angular direction on our spherical-polar grid, models then explode, and explode slightly more vigorously with increasing resolution. This suggests that there can be a qualitative dependence of the outcome of 3D CCSN simulations upon spatial resolution. The critical aspect of higher spatial resolution is the adequate capturing of the physics of neutrino-driven turbulence, in particular its Reynolds stress. The greater numerical viscosity of lower resolution simulations results in greater drag on the turbulent eddies that embody turbulent stress, and, hence, in a diminution of their vigor. Turbulent stress not only pushes the temporarily stalled shock further out, but bootstraps a concomitant increase in the deposited neutrino power. Both effects together lie at the core of the resolution dependence we observe.