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Abstract Unconventional superconductors have Cooper pairs with lower symmetries than in conventional superconductors. In most unconventional superconductors, the additional symmetry breaking occurs in relation to typical ingredients such as strongly correlated Fermi liquid phases, magnetic fluctuations, or strong spin-orbit coupling in noncentrosymmetric structures. In this article, we show that the time-reversal symmetry breaking in the superconductor LaNiGa 2 is enabled by its previously unknown topological electronic band structure, with Dirac lines and a Dirac loop at the Fermi level. Two symmetry related Dirac points even remain degenerate under spin-orbit coupling. These unique topological features enable an unconventional superconducting gap in which time-reversal symmetry can be broken in the absence of other typical ingredients. Our findings provide a route to identify a new type of unconventional superconductors based on nonsymmorphic symmetries and will enable future discoveries of topological crystalline superconductors. 

Isolated pyramids, 30–80 nm wide and 3–20 nm tall, form during sputter-annealing cycles on the Ge (110) surface. Pyramids have four walls with {19 13 1} faceting and a steep mound at the apex. We used scanning tunneling microscopy (STM) under ultrahigh vacuum conditions to periodically image the surface at ion energies between 100 eV and 500 eV and incremental total flux. Pyramids are seen using Ar+ between 200 eV and 400 eV, and require Ag to be present on the sample or sample holder. We suspect that the pyramids are initiated by Ag co-sputtered onto the surface. Growth of pyramids is due to the gathering of step edges with (16 × 2) reconstruction around the pyramid base during layer-by-layer removal of the substrate, and conversion to {19 13 1} faceting. The absence of pyramids using Ar+ energies above 400 eV is likely due to surface damage that is insufficiently annealed. 

ABSTRACT Separating galactic foreground emission from maps of the cosmic microwave background (CMB) and quantifying the uncertainty in the CMB maps due to errors in foreground separation are important for avoiding biases in scientific conclusions. Our ability to quantify such uncertainty is limited by our lack of a model for the statistical distribution of the foreground emission. Here, we use a deep convolutional generative adversarial network (DCGAN) to create an effective non-Gaussian statistical model for intensity of emission by interstellar dust. For training data we use a set of dust maps inferred from observations by the Planck satellite. A DCGAN is uniquely suited for such unsupervised learning tasks as it can learn to model a complex non-Gaussian distribution directly from examples. We then use these simulations to train a second neural network to estimate the underlying CMB signal from dust-contaminated maps. We discuss other potential uses for the trained DCGAN, and the generalization to polarized emission from both dust and synchrotron.