Code generation for AMReX with applications to numerical relativity

We present a new python/SymPy based code generator for producing executable numerical expressions for partial differential equations in AMReX-based applications. We demonstrate the code generator capabilities for the case of $3+1$ ADM formulations of numerical relativity for the constraint damped, conformal Z4 formulations (Z4c and CCZ4). The generated spacetime solvers are examined for stability and accuracy using a selection of checks from the standard Apples with Apples testbeds for numerical relativity applications. We also explore physically interesting vacuum spacetimes including head-on and inspiraling black hole binary collisions, and investigate the simulated gravitational waveforms from such events with the Newman-Penrose formulation of waveform extraction.Comment: 26 pages, 15 figure

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies

Particle-in-cell simulation of the neutrino fast flavor instability

Neutrinos drive core-collapse supernovae, launch outflows from neutron star merger accretion disks, and set the ratio of protons to neutrons in ejecta from both systems that generate heavy elements in the universe. Neutrinos of different flavors interact with matter differently, and much recent work has suggested that fast flavor instabilities are likely ubiquitous in both systems, but the final flavor content after the instability saturates has not been well understood. In this work we present particle-in-cell calculations which follow the evolution of all flavors of neutrinos and antineutrinos through saturation and kinematic decoherence. We conduct one-dimensional three-flavor simulations of neutrino quantum kinetics to demonstrate the outcome of this instability in a few example cases. We demonstrate the growth of both axially symmetric and asymmetric modes whose wavelength and growth rate match predictions from linear stability analysis. Finally, we vary the number density, flux magnitude, and flux direction of the neutrinos and antineutrinos and demonstrate that these factors modify both the growth rate and post-saturation neutrino flavor abundances. Weak electron lepton number (ELN) crossings in these simulations produce both slow growth of the instability and little difference between the flavor abundances in the initial and final states. In all of these calculations the same number of neutrinos and antineutrinos change flavor, making the least abundant between them the limiting factor for post-saturation flavor change. Many more simulations and multi-dimensional simulations are needed to fully probe the parameter space of the initial conditions

Dark matter from axion strings with adaptive mesh refinement.

Axions are hypothetical particles that may explain the observed dark matter density and the non-observation of a neutron electric dipole moment. An increasing number of axion laboratory searches are underway worldwide, but these efforts are made difficult by the fact that the axion mass is largely unconstrained. If the axion is generated after inflation there is a unique mass that gives rise to the observed dark matter abundance; due to nonlinearities and topological defects known as strings, computing this mass accurately has been a challenge for four decades. Recent works, making use of large static lattice simulations, have led to largely disparate predictions for the axion mass, spanning the range from 25 microelectronvolts to over 500 microelectronvolts. In this work we show that adaptive mesh refinement simulations are better suited for axion cosmology than the previously-used static lattice simulations because only the string cores require high spatial resolution. Using dedicated adaptive mesh refinement simulations we obtain an over three order of magnitude leap in dynamic range and provide evidence that axion strings radiate their energy with a scale-invariant spectrum, to within ~5% precision, leading to a mass prediction in the range (40,180) microelectronvolts

Predicting reliability through structured expert elicitation with repliCATS (Collaborative Assessments for Trustworthy Science)

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Data from a pilot experiment supports the validity of this process, with accuracy that meets or exceeds that of other techniques used to predict replicability while providing additional benefits. The repliCATS process is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to assist with problems like understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies