Galactic rotation curves exhibit diverse behavior in the inner regions, while obeying an organizing principle, i.e., they can be approximately described by a radial acceleration relation or the Modified Newtonian Dynamics phenomenology. We analyze the rotation curve data from the SPARC sample, and explicitly demonstrate that both the diversity and uniformity are naturally reproduced in a hierarchical structure formation model with the addition of dark matter self-interactions. The required concentrations of the dark matter halos are fully consistent with the concentration-mass relation predicted by the Planck cosmological model. The inferred stellar massto-light (3.6 µm) ratios scatter around 0.5M /L , as expected from population synthesis models, leading to a tight radial acceleration relation and baryonic Tully-Fisher relation. The inferred stellar-halo mass relation is consistent with the expectations from abundance matching. These results indicate that the inner dark matter halos of galaxies are thermalized due to the self-interactions of dark matter particles.

We present a new extended gamma ray excess detected with the Fermi Satellite Large Area Telescope toward the Galactic Center that traces the morphology of infrared starlight emission. Combined with its measured spectrum, this new extended source is approximately consistent with inverse Compton emission from a high-energy electron-positron population with energies up to about 10 GeV. Previously detected emissions tracing the 20 cm radio, interpreted as bremsstrahlung radiation, and the Galactic Center Extended emission tracing a spherical distribution and peaking at 2 GeV, are also detected. We show that the inverse Compton and bremsstrahlung emissions are likely due to the same source of electrons and positrons. All three extended emissions may be explained within the framework of a model where the dark matter annihilates to leptons or a model with unresolved millisecond pulsars in the Galactic Center.

We study the atomic physics and the astrophysical implications of a model in which the dark matter is the analog of hydrogen in a secluded sector. The self-interactions between dark matter particles include both elastic scatterings as well as inelastic processes due to a hyperfine transition. The self-interaction cross sections are computed by numerically solving the coupled Schrödinger equations for this system. We show that these self-interactions exhibit the right velocity dependence to explain the low dark matter density cores seen in small galaxies while being consistent with all constraints from observations of clusters of galaxies. For a viable solution, the dark hydrogen mass has to be in 10-100 GeV range and the dark fine-structure constant has to be larger than 0.01. This range of model parameters requires the existence of a dark matter-anti-matter asymmetry in the early universe to set the relic abundance of dark matter. For this range of parameters, we show that significant cooling losses may occur due to inelastic excitations to the hyperfine state and subsequent decays, with implications for the evolution of low-mass halos and the early growth of supermassive black holes. Cooling from excitations to higher n levels of dark hydrogen and subsequent decays is possible at the cluster scale, with a strong dependence on halo mass. Finally, we show that the minimum halo mass is in the range of 10 3.5 to 10 7 M for the viable regions of parameter space, significantly larger than the typical predictions for weakly interacting dark matter models. This pattern of observables in cosmological structure formation is unique to this model, making it possible to rule in or rule out hidden sector hydrogen as a viable dark matter model.

We perform a composite likelihood analysis of subdivided regions within the central 26 • ×20 • of the Milky Way, with the aim of characterizing the spectrum of the gamma-ray galactic center excess in regions of varying galactocentric distance. Outside of the innermost few degrees, we find that the radial profile of the excess is background-model dependent and poorly constrained. The spectrum of the excess emission is observed to extend upwards of 10 GeV outside ∼ 5 • in radius, but cuts off steeply between 10-20 GeV only in the innermost few degrees. If interpreted as a real feature of the excess, this radial variation in the spectrum has important implications for both astrophysical and dark matter interpretations of the galactic center excess. Single-component dark matter annihilation models face challenges in reproducing this variation; on the other hand, a population of unresolved millisecond pulsars contributing both prompt and secondary inverse Compton emission may be able to explain the spectrum as well as its spatial dependency. We show that the expected differences in the photon-count distributions of a smooth dark matter annihilation signal and an unresolved point source population are an order of magnitude smaller than the fluctuations in residuals after fitting the data, which implies that mismodeling is an important systematic effect in point source analyses aimed at resolving the gamma-ray excess.

Despite steady improvements in the skill of numerical weather and climate models over the last decades, a longstanding issue is the development of biases after initialization. These biases (systematic forecast errors) cause degradation of performance for both medium range weather forecasting and subseasonal to decadal climate predictions. They arise from issues like limited resolution, inaccurate physical parameterizations, and imperfect initial conditions. Typically, postprocessing steps are developed to handle these biases such as model output statistics for weather forecasting (Glahn & Lowry, 1972) or ensemble bias correction for seasonal prediction (Arribas et al., 2011;Stockdale et al., 1988). In this study, we propose an online bias correction method using machine learning (ML). We apply a corrective tendency to the prognostic state of the atmospheric model at each time step in order to reduce atmospheric model error growth. The necessary corrective tendencies are estimated from a hindcast simulation which is linearly nudged towards an observational analysis. An ML model is trained to predict the nudging tendencies using only the state of the model as inputs. This ML model can then be used in a forecast to keep the model evolution on a more realistic manifold.