Within the Minimal Supersymmetric Standard Model (MSSM), LHC bounds suggest that scalar superpartner masses are far above the electroweak scale. Given a high superpartner mass, nonthermal dark matter is a viable alternative to WIMP dark matter generated via freezeout. In the presence of moduli fields nonthermal dark matter production is associated with a long matter dominated phase, modifying the spectral index and primordial tensor amplitude relative to those in a thermalized primordial universe. Nonthermal dark matter can have a higher selfinteraction cross-section than its thermal counterpart, enhancing astrophysical bounds on its annihilation signals. We constrain the contributions to the neutralino mass from the bino, wino and higgsino using existing astrophysical bounds and direct detection experiments for models with nonthermal neutralino dark matter. Using these constraints we quantify the expected change to inflationary observables resulting from the nonthermal phase.

In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the appendix for access to the dataset.

We make a first study of the phase diagram of four-dimensional N = 4 super Yang-Mills theory regulated on a space-time lattice. The lattice formulation we employ is both gauge invariant and retains at all lattice spacings one exactly preserved supersymmetry charge. Our numerical results are consistent with the existence of a single deconfined phase at all observed values of the bare coupling.

In recent years a new class of supersymmetric lattice theories have been proposed which retain one or more exact supersymmetries for non-zero lattice spacing. Recently there has been some controversy in the literature concerning whether these theories suffer from a sign problem. In this paper we address this issue by conducting simulations of the N = (2, 2) and N = (8, 8) supersymmetric Yang-Mills theories in two dimensions for the U (N ) theories with N = 2, 3, 4, using the new twisted lattice formulations. Our results provide evidence that these theories do not suffer from a sign problem in the continuum limit. These results thus boost confidence that the new lattice formulations can be used successfully to explore non-perturbative aspects of four-dimensional N = 4 supersymmetric Yang-Mills theory.

The presence of certain elements within a star, and by extension its planet, strongly impacts the formation and evolution of the planetary system. The positive correlation between a host star's ironcontent and the presence of an orbiting giant exoplanet has been confirmed (e.g. . However, the importance of other elements in predicting giant planet occurrence is less certain despite their central role in shaping internal planetary structure. In order to understand the subtle, yet crucial way that non-iron elements may influence the formation of giant planets, we apply advances in data-driven research to the Hypatia Catalog (Hinkel et al. 2014) of stellar abundances. We designed a machine learning algorithm to analyze stellar abundance patterns of known host stars, similar to how online streaming services use viewer history to recommend movies, to determine those elements important in identifying potential giant exoplanet host stars. We analyzed a variety of scenarios involving different groups of elements, namely volatiles (C, O), lithophiles (Na, Mg, Al, Si, Ca, Sc, Ti, V, Mn, Y), siderophiles (Cr, Co, Ni), and Fe. Here we show that oxygen, carbon, and sodium, besides iron, are influential indicators of a giant planet and we present a list of ∼350 stars that have a ≥90% probability of hosting a giant exoplanet. We anticipate that our findings will revolutionize the determination of interior structure models for both giant and terrestrial planets. Furthermore, our results demonstrate how this planet-finding algorithm can be used to guide future target lists, such as the TESS, CHEOPS, JWST, and WFIRST missions.