Neutrino processes in partially degenerate neutron matter

We investigate neutrino processes for conditions reached in simulations of core-collapse supernovae. Where neutrino-matter interactions play an important role, matter is partially degenerate, and we extend earlier work that addressed the degenerate regime. We derive expressions for the spin structure factor in neutron matter, which is a key quantity required for evaluating rates of neutrino processes. We show that, for essentially all conditions encountered in the post-bounce phase of core-collapse supernovae, it is a very good approximation to calculate the spin relaxation rates in the nondegenerate limit. We calculate spin relaxation rates based on chiral effective field theory interactions and find that they are typically a factor of two smaller than those obtained using the standard one-pion-exchange interaction alone.Comment: 41 pages, 9 figures, NORDITA-2011-116; added comparison figures and fit function for use in simulations, to appear in Astrophys.

Cohesive crack approach to debonding analysis

Debonding of coatings from substrate due to coating compression occurs in many engineering applications. A simplified analytical approach for the estimation of the ultimate coating compression leading to debonding is developed in this paper, assuming an assigned out-of-plane defect of the coating. The formulation is based on the solution of a beam on a Pasternak (two parameters) elastic foundation, and on the assumption of a Mode I cohesive failure of the coating-substrate interface. The resulting formulas are simple and require the knowledge of a limited number of parameters

A hybrid Lagrangian-Eulerian particle finite element method for free-surface and fluid-structure interaction problems

The dynamics of fluid flows with free surfaces and interacting with highly deformable structures is a complex problem, attracting considerable attention. The Particle Finite Element Method (PFEM) is one of the various numerical methods recently proposed in the literature to simulate this type of problems. It is a mesh-based Lagrangian approach, particularly suited for problems with fast changes in the domain topology, since the fluid boundaries and the Fluid-Structure Interaction (FSI) interface are naturally tracked by the position of the mesh nodes. However, when nonhomogeneous boundary conditions are imposed on velocities or when there are regions where the topology varies moderately, for example, in confined portions of the fluid domain characterized by fixed boundaries, an Eulerian formulation turns out to be more convenient. To exploit the advantages of both formulations, an adaptive hybrid Lagrangian-Eulerian approach is presented in this work. According to the proposed method, nodes on the fluid free-surface and on the FSI interface are treated as Lagrangian, while the remaining nodes can be either Eulerian or Lagrangian. Furthermore, to increase the efficiency of the method, an algorithm to automatically detect runtime the transition zone between the two kinematic descriptions is devised. To validate the proposed approach, several numerical examples are developed and their results are compared to those available in the literature

Pushing 1D CCSNe to explosions: model and SN 1987A

We report on a method, PUSH, for triggering core-collapse supernova explosions of massive stars in spherical symmetry. We explore basic explosion properties and calibrate PUSH such that the observables of SN1987A are reproduced. Our simulations are based on the general relativistic hydrodynamics code AGILE combined with the detailed neutrino transport scheme IDSA for electron neutrinos and ALS for the muon and tau neutrinos. To trigger explosions in the otherwise non-exploding simulations, we rely on the neutrino-driven mechanism. The PUSH method locally increases the energy deposition in the gain region through energy deposition by the heavy neutrino flavors. Our setup allows us to model the explosion for several seconds after core bounce. We explore the progenitor range 18-21M$_{\odot}$. Our studies reveal a distinction between high compactness (HC) and low compactness (LC) progenitor models, where LC models tend to explore earlier, with a lower explosion energy, and with a lower remnant mass. HC models are needed to obtain explosion energies around 1 Bethe, as observed for SN1987A. However, all the models with sufficiently high explosion energy overproduce $^{56}$Ni. We conclude that fallback is needed to reproduce the observed nucleosynthesis yields. The nucleosynthesis yields of $^{57-58}$Ni depend sensitively on the electron fraction and on the location of the mass cut with respect to the initial shell structure of the progenitor star. We identify a progenitor and a suitable set of PUSH parameters that fit the explosion properties of SN1987A when assuming 0.1M$_{\odot}$ of fallback. We predict a neutron star with a gravitational mass of 1.50M$_{\odot}$. We find correlations between explosion properties and the compactness of the progenitor model in the explored progenitors. However, a more complete analysis will require the exploration of a larger set of progenitors with PUSH.Comment: revised version as accepted by ApJ (results unchanged, text modified for clarification, a few references added); 26 pages, 20 figure

GAM Forest Explanation

Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In this work we focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests. We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions. We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset