Angular correlations in single-top-quark and Wjj production at next-to-leading order

I demonstrate that the correlated angular distributions of final-state particles in both single-top-quark production and the dominant Wjj backgrounds can be reliably predicted. Using these fully-correlated angular distributions, I propose a set of cuts that can improve the single-top-quark discovery significance by 25%, and the signal to background ratio by a factor of 3 with very little theoretical uncertainty. Up to a subtlety in t-channel single-top-quark production, leading-order matrix elements are shown to be sufficient to reproduce the next-to-leading order correlated distributions.Comment: 22 pages, 23 figs, RevTex4, fixed typos, to appear in Phys. Rev.

Standard model explanation of a CDF dijet excess in Wjj

We demonstrate the recent observation of a peak in the dijet invariant mass of the Wjj signal observed by the CDF Collaboration can be explained as the same upward fluctuation observed by CDF in single-top-quark production. In general, both t-channel and s-channel single-top-quark production produce kinematically induced peaks in the dijet spectrum. Since CDF used a Monte Carlo simulation to subtract the single-top backgrounds instead of data, a peak in the dijet spectrum is expected. The D0 Collaboration has a small upward fluctuation in their published t-channel data; and hence we predict they would see at most a small peak in the dijet invariant mass spectrum of Wjj if they follow the same procedure as CDF.Comment: 3 pg., 2 figs, revtex, minor clarifications, to appear in Phys. Rev.

Development of high resolution simulations of the atmospheric environment using the MASS model

Numerical simulations were performed with a very high resolution (7.25 km) version of the MASS model (Version 4.0) in an effort to diagnose the vertical wind shear and static stability structure during the Shuttle Challenger disaster which occurred on 28 January 1986. These meso-beta scale simulations reveal that the strongest vertical wind shears were concentrated in the 200 to 150 mb layer at 1630 GMT, i.e., at about the time of the disaster. These simulated vertical shears were the result of two primary dynamical processes. The juxtaposition of both of these processes produced a shallow (30 mb deep) region of strong vertical wind shear, and hence, low Richardson number values during the launch time period. Comparisons with the Cape Canaveral (XMR) rawinsonde indicates that the high resolution MASS 4.0 simulation more closely emulated nature than did previous simulations of the same event with the GMASS model

The price ain’t right? Hospital prices and health spending on the privately insured

We use insurance claims data covering 28% of individuals with employer-sponsored health insurance in the United States to study the variation in health spending on the privately insured, examine the structure of insurer-hospital contracts, and analyze the variation in hospital prices across the nation. Health spending per privately insured beneficiary differs by a factor of three across geographic areas and has a very low correlation with Medicare spending. For the privately insured, half of the spending variation is driven by price variation across regions, and half is driven by quantity variation. Prices vary substantially across regions, across hospitals within regions, and even within hospitals. For example, even for a nearly homogeneous service such as lower-limb magnetic resonance imaging, about a fifth of the total case-level price variation occurs within a hospital in the cross section. Hospital market structure is strongly associated with price levels and contract structure. Prices at monopoly hospitals are 12% higher than those in markets with four or more rivals. Monopoly hospitals also have contracts that load more risk on insurers (e.g., they have more cases with prices set as a share of their charges). In concentrated insurer markets the opposite occurs—hospitals have lower prices and bear more financial risk. Examining the 366 mergers and acquisitions that occurred between 2007 and 2011, we find that prices increased by over 6% when the merging hospitals were geographically close (e.g., 5 miles or less apart), but not when the hospitals were geographically distant (e.g., over 25 miles apart)