University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Enhanced sensitivity of the LIGO gravitational wave detector by using squeezed states of light

Abstract: Nearly a century after Einstein first predicted the existence of gravitational waves, a global network of Earth-based gravitational wave observatories1, 2, 3, 4 is seeking to directly detect this faint radiation using precision laser interferometry. Photon shot noise, due to the quantum nature of light, imposes a fundamental limit on the attometre-level sensitivity of the kilometre-scale Michelson interferometers deployed for this task. Here, we inject squeezed states to improve the performance of one of the detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) beyond the quantum noise limit, most notably in the frequency region down to 150 Hz, critically important for several astrophysical sources, with no deterioration of performance observed at any frequency. With the injection of squeezed states, this LIGO detector demonstrated the best broadband sensitivity to gravitational waves ever achieved, with important implications for observing the gravitational-wave Universe with unprecedented sensitivity.

A Meta-Analysis of Hypothetical Bias in Stated Preference Valuation

Abstract: Individuals are widely believed to overstate their economic valuation of a good by a factor of two or three. This paper reports the results of a meta-analysis of hypothetical bias in 28 stated preference valuation studies that report monetary willingness-to-pay and that used the same mechanism for eliciting both hypothetical and actual values. The papers generated 83 observations with a median value of the ratio of hypothetical to actual value of 1.35, and the distribution has severe positive skewness. Since a comprehensive theory of hypothetical bias has not been developed, we use a set of explanatory variables based on issues that have been investigated in previous research. We find that a choice-based elicitation mechanism is important in reducing bias, though an insufficient number of studies and confounding with other variables prevents us from characterizing individual mechanisms. We provide some evidence that the use of student subjects may be a source of bias, but this variable is highly correlated with group experimental settings and no firm conclusions can be drawn. There is some weak evidence that bias increases when public goods are being valued, and that some calibration methods are effective at reducing bias. Results are quite sensitive to model specification, which will remain a problem until a comprehensive theory of hypothetical bias is developed.

A System to Generate Test Data and Symbolically Execute Programs

Abstract: This paper describes a system that attempts to generate test data for programs written in ANSI Fortran. Given a path, the system symbolically executes the path and creates a set of constraints on the program's input variables. If the set of constraints is linear, linear programming techniques are employed to obtain a solution. A solution to the set of constraints is test data that will drive execution down the given path. If it can be determined that the set of constraints is inconsistent, then the given path is shown to be nonexecutable. To increase the chance of detecting some of the more common programming errors, artificial constraints are temporarily created that simulate error conditions and then an attempt is made to solve each augmented set of constraints. A symbolic representation of the program's output variables in terms of the program's input variables is also created. The symbolic representation is in a human readable form that facilitates error detection as well as being a possible aid in assertion generation and automatic program documentation.