Fitting the Gamma-Ray Spectrum from Dark Matter with DMFIT: GLAST and the Galactic Center Region

We study the potential of GLAST to unveil particle dark matter properties with gamma-ray observations of the Galactic center region. We present full GLAST simulations including all gamma-ray sources known to date in a region of 4 degrees around the Galactic center, in addition to the diffuse gamma-ray background and to the dark matter signal. We introduce DMFIT, a tool that allows one to fit gamma-ray emission from pair-annihilation of generic particle dark matter models and to extract information on the mass, normalization and annihilation branching ratios into Standard Model final states. We assess the impact and systematic effects of background modeling and theoretical priors on the reconstruction of dark matter particle properties. Our detailed simulations demonstrate that for some well motivated supersymmetric dark matter setups with one year of GLAST data it will be possible not only to significantly detect a dark matter signal over background, but also to estimate the dark matter mass and its dominant pair-annihilation mode.Comment: 37 pages, 16 figures, submitted to JCA

Ultrahigh Energy Cosmic Rays: The state of the art before the Auger Observatory

In this review we discuss the important progress made in recent years towards understanding the experimental data on cosmic rays with energies \agt 10^{19} eV. We begin with a brief survey of the available data, including a description of the energy spectrum, mass composition, and arrival directions. At this point we also give a short overview of experimental techniques. After that, we introduce the fundamentals of acceleration and propagation in order to discuss the conjectured nearby cosmic ray sources. We then turn to theoretical notions of physics beyond the Standard Model where we consider both exotic primaries and exotic physical laws. Particular attention is given to the role that TeV-scale gravity could play in addressing the origin of the highest energy cosmic rays. In the final part of the review we discuss the potential of future cosmic ray experiments for the discovery of tiny black holes that should be produced in the Earth's atmosphere if TeV-scale gravity is realized in Nature.Comment: Final version. To be published in Int. J. Mod. Phys.

Determining Supersymmetric Parameters With Dark Matter Experiments

In this article, we explore the ability of direct and indirect dark matter experiments to not only detect neutralino dark matter, but to constrain and measure the parameters of supersymmetry. In particular, we explore the relationship between the phenomenological quantities relevant to dark matter experiments, such as the neutralino annihilation and elastic scattering cross sections, and the underlying characteristics of the supersymmetric model, such as the values of mu (and the composition of the lightest neutralino), m_A and tan beta. We explore a broad range of supersymmetric models and then focus on a smaller set of benchmark models. We find that by combining astrophysical observations with collider measurements, mu can often be constrained far more tightly than it can be from LHC data alone. In models in the A-funnel region of parameter space, we find that dark matter experiments can potentially determine m_A to roughly +/-100 GeV, even when heavy neutral MSSM Higgs bosons (A, H_1) cannot be observed at the LHC. The information provided by astrophysical experiments is often highly complementary to the information most easily ascertained at colliders.Comment: 46 pages, 76 figure

A Search for Selectrons and Squarks at HERA

Data from electron-proton collisions at a center-of-mass energy of 300 GeV are used for a search for selectrons and squarks within the framework of the minimal supersymmetric model. The decays of selectrons and squarks into the lightest supersymmetric particle lead to final states with an electron and hadrons accompanied by large missing energy and transverse momentum. No signal is found and new bounds on the existence of these particles are derived. At 95% confidence level the excluded region extends to 65 GeV for selectron and squark masses, and to 40 GeV for the mass of the lightest supersymmetric particle.Comment: 13 pages, latex, 6 Figure

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages