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Self-interactions within the dark sector could clump dark matter into heavy composite states with low number density, leading to a highly suppressed event rate in existing direct detection experiments. However, the large interaction cross section between such ultra-heavy dark matter (UHDM) and standard model matter results in a distinctive and compelling signature: long, straight damage tracks as they pass through and scatter with matter. In this work, we propose using geologically old quartz samples as large-exposure detectors for UHDM. We describe a high-resolution readout method based on electron microscopy, characterize the most favorable geological samples for this approach, and study its reach in a simple model of the dark sector. The advantage of this search strategy is two-fold: the age of geological quartz compensates for the low number density of UHDMs, and the distinct geometry of the damage track serves as a high-fidelity background rejection tool., Comment: 9 pages, 3 figures

We propose an algorithm based on Hilbert space-filling curves to reorder mesh elements in memory for use with the Spectral Element Method, aiming to attain fewer cache misses, better locality of data reference and faster execution. We present a technique to numerically simulate acoustic wave propagation in 2D domains using the Spectral Element Method, and discuss computational performance aspects of this procedure. We reorder mesh-related data via Hilbert curves to achieve sizable reductions in execution time under several mesh configurations in shared-memory systems. Our experiments show that the Hilbert curve approach works well with meshes of several granularities and also with small and large variations in element sizes, achieving reductions between 9% and 25% in execution time when compared to three other ordering schemes., Comment: 23 pages, 12 figures

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

We review recent advances in our understanding of magnetism in the solar nebular and protoplanetary disks (PPDs). We discuss the implications of theory, meteorite measurements, and astronomical observations for planetary formation and nebular evolution. Paleomagnetic measurements indicate the presence of fields of 0.54$\pm$0.21 G at $\sim$1 to 3 astronomical units (AU) from the Sun and $\gtrsim$0.06 G at 3 to 7 AU until >1.22 and >2.51 million years (Ma) after solar system formation, respectively. These intensities are consistent with those predicted to enable typical astronomically-observed protostellar accretion rates of $\sim$10$^{-8}$ M$_\odot$ yr$^{-1}$, suggesting that magnetism played a central role in mass and angular momentum transport in PPDs. Paleomagnetic studies also indicate fields <0.006 G and <0.003 G in the inner and outer solar system by 3.94 and 4.89 Ma, respectively, consistent with the nebular gas having dispersed by this time. This is similar to the observed lifetimes of extrasolar protoplanetary disks., Comment: Revised version published in Science Advances

We report parametric resonances (PRs) in the mean-field dynamics of a one-dimensional dipolar Bose-Einstein condensate (DBEC) in widely varying trapping geometries. The chief goal is to characterize the energy levels of this system by analytical methods and the significance of this study arises from the commonly known fact that in the presence of interactions the energy levels of a trapped BEC are hard to calculate analytically. The latter characterization is achieved by a matching of the PR energies to energy levels of the confining trap using perturbative methods. Further, this work reveals the role of the interplay between dipole-dipole interactions (DDI) and trapping geometry in defining the energies and amplitudes of the PRs. The PRs are induced by a negative Gaussian potential whose depth oscillates with time. Moreover, the DDI play a role in this induction. The dynamics of this system is modeled by the time-dependent Gross- Pitaevskii equation (TDGPE) that is numerically solved by the Crank-Nicolson method. The PRs are discussed basing on analytical methods: first, it is shown that it is possible to reproduce PRs by the Lagrangian variational method that are similar to the ones obtained from TDGPE. Second, the energies at which the PRs arise are closely matched with the energy levels of the corresponding trap calculated by time-independent perturbation theory. Third, the most probable transitions between the trap energy levels yielding PRs are determined by time-dependent perturbation theory. The most significant result of this work is that we have been able to characterize the above mentioned energy levels of a DBEC in a complex trapping potential., Comment: 6 figures, 18 pages

With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering that are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long-term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation., Comment: 15 pages

We provide an overview of the experimental techniques, measurement modalities, and diverse applications of the Quantum Diamond Microscope (QDM). The QDM employs a dense layer of fluorescent nitrogen-vacancy (NV) color centers near the surface of a transparent diamond chip on which a sample of interest is placed. NV electronic spins are coherently probed with microwaves and optically initialized and read out to provide spatially resolved maps of local magnetic fields. NV fluorescence is measured simultaneously across the diamond surface, resulting in a wide-field, two-dimensional magnetic field image with adjustable spatial pixel size set by the parameters of the imaging system. NV measurement protocols are tailored for imaging of broadband and narrowband fields, from DC to GHz frequencies. Here we summarize the physical principles common to diverse implementations of the QDM and review example applications of the technology in geoscience, biology, and materials science.