Related papers: Applications of Machine Learning to Predicting Cor…
We explore the potential of current and future liquid scintillator neutrino detectors of O (10) kt mass to localize a pre-supernova neutrino signal in the sky. In the hours preceding the core collapse of a nearby star (at distance D < 1…
In this work, we study the synthetic explosions of a massive star. We take a 100 M$_{\odot}$ zero--age main--sequence (ZAMS) star and evolve it until the onset of core-collapse using {\tt MESA}. Then, the resulting star model is exploded…
Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing…
Recent multi-dimensional simulations of core-collapse supernovae are producing successful explosions and explosion-energy predictions. In general, the explosion-energy evolution is monotonic and relatively smooth, suggesting a possible…
We present results from three-dimensional, magnetohydrodynamic, core-collapse simulations of sixteen progenitors following until 0.5 s after bounce. We use non-rotating solar-metallicity progenitor models with zero-age main-sequence mass…
We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as Wavelet Detection…
We perform the first self-consistent, time-dependent, multi-group calculations in two dimensions (2D) to address the consequences of using the ray-by-ray+ transport simplification in core-collapse supernova simulations. Such a dimensional…
The evolutionary pathways of core-collapse supernova progenitors at the low-mass end of the spectrum are beset with major uncertainties. In recent years, a variety of evolutionary channels has been discovered in addition to the classical…
Non-spherical structure in massive stars at the point of iron core collapse can have a qualitative impact on the properties of the ensuing core-collapse supernova explosions and the multi-messenger signals they produce. Strong perturbations…
Type II supernovae (SNe II) are the most frequently observed outcome of core-collapse explosions and provide a valuable window into the physical mechanisms governing the deaths of massive stars. However, estimates of explosion properties…
We use numerical hydrodynamic simulations to investigate prestellar core formation in the dynamic environment of giant molecular clouds, focusing on planar post-shock layers produced by colliding turbulent flows. A key goal is to test how…
We revisit the diffuse supernova neutrino background in light of recent systematic studies of stellar core collapse that reveal the quantitative impacts of the progenitor conditions on the collapse process. In general, the dependence of the…
This work utilizes established models of synchrotron-powered light curves for core-collapse supernovae in dense circumstellar environments, namely type IIn and Ibn, to demonstrate the potential for detecting millimeter emission from these…
An image-based deep learning framework is developed in this paper to predict damage and failure in microstructure-dependent composite materials. The work is motivated by the complexity and computational cost of high-fidelity simulations of…
A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amidst a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results…
The explosion mechanism of a core-collapse supernova is a complex interplay between neutrino heating and cooling (including the effects of neutrino-driven convection), the gravitational potential, and the ram pressure of the infalling…
Nucleosynthetic yield predictions for multi-dimensional simulations of thermonuclear supernovae generally rely on the tracer particle method to obtain isotopic information of the ejected material for a given supernova simulation. We…
We present a new observational method to type the explosions of young supernova remnants (SNRs). By measuring the morphology of the Chandra X-ray line emission in seventeen Galactic and Large Magellanic Cloud SNRs with a multipole expansion…
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular…
We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble…