Nearly Degenerate Gauginos and Dark Matter at the LHC

Motivated by dark-matter considerations in supersymmetric theories, we investigate in a fairly model-independent way the detection at the LHC of nearly degenerate gauginos with mass differences between a few GeV and about 30 GeV. Due to the degeneracy of gaugino states, the conventional leptonic signals are likely lost. We first consider the leading signal from gluino production and decay. We find that it is quite conceivable to reach a large statistical significance for the multi-jet plus missing energy signal with an integrated luminosity about 50 pb^-1 (50 fb^-1) for a gluino mass of 500 GeV (1 TeV). If gluinos are not too heavy, less than about 1.5 TeV, this channel can typically probe gaugino masses up to about 100 GeV below the gluino mass. We then study the Drell-Yan type of gaugino pair production in association with a hard QCD jet, for gaugino masses in the range of 100-150 GeV. The signal observation may be statistically feasible with about 10 fb^-1, but systematically challenging due to the lack of distinctive features for the signal distributions. By exploiting gaugino pair production through weak boson fusion, signals of large missing energy plus two forward-backward jets may be observable at a 4-6\sigma level above the large SM backgrounds with an integrated luminosity of 100-300 fb^-1. Finally, we point out that searching for additional isolated soft muons in the range p_T ~3-10 GeV in the data samples discussed above may help to enrich the signal and to control the systematics. Significant efforts are made to explore the connection between the signal kinematics and the relevant masses for the gluino and gauginos, to probe the mass scales of the superpartners, in particular the LSP dark matter.Comment: 35 pages, 32 figure

Conditional forecasts in dynamic multivariate models

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.Econometric models ; Forecasting ; Time-series analysis

Phase Transitions in the NMSSM

We study phase transitions in the Next-to-Minimal Supersymmetric Standard Model (NMSSM) with the weak scale vacuum expectation values of the singlet scalar, constrained by Higgs spectrum and vacuum stability. We find four different types of phase transitions, three of which have two-stage nature. In particular, one of the two-stage transitions admits strongly first order electroweak phase transition, even with heavy squarks. We introduce a tree-level explicit CP violation in the Higgs sector, which does not affect the neutron electric dipole moment. In contrast to the MSSM with the CP violation in the squark sector, a strongly first order phase transition is not so weakened by this CP violation.Comment: 21 pages, 8 figure

Likelihood-preserving normalization in multiple equation models

Causal analysis in multiple equation models often revolves around the evaluation of the effects of an exogenous shift in a structural equation. When taking into account the uncertainty implied by the shape of the likelihood, we argue that how normalization is implemented matters for inferential conclusions around the maximum likelihood (ML) estimates of such effects. We develop a general method that eliminates the distortion of finite-sample inferences about these ML estimates after normalization. We show that our likelihood-preserving normalization always maintains coherent economic interpretations while an arbitrary implementation of normalization can lead to ill-determined inferential results.Time-series analysis ; Supply and demand ; Demand for money ; Money supply

A Gibbs simulator for restricted VAR models

Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of linear restrictions than those in the existing literature. We show that the required computation is of an SUR type, and thus our method can be implemented cheaply even for large systems of multiple equations.Econometric models ; Vector autoregression ; Monetary policy ; Time-series analysis

CP Violation in the Higgs Sector and Phase Transition in the MSSM

We investigate the electroweak phase transition in the presence of a large CP violation in the squark sector of the MSSM. When the CP violation is large, scalar-pseudoscalar mixing of the Higgs bosons occurs and a large CP violation in the Higgs sector is induced. It, however, weakens first-order phase transition before the mixing reaches the maximal. Even when the CP violation in the squark sector is not so large that the phase transition is strongly first order, the phase difference between the broken and symmetric phase regions grows to O(1), which leads to successful baryogenesis, when the charged Higgs bosons is light.Comment: 18 pages, 6 figures, LaTeX2

Confronting Model Misspecification in Macroeconomics

We estimate a Markov-switching mixture of two familiar macroeconomic models: a richly parameterized DSGE model and a corresponding BVAR model. We show that the Markov-switching mixture model dominates both individual models and improves the fit considerably. Our estimation indicates that the DSGE model plays an important role only in the late 1970s and the early 1980s. We show how to use the mixture model as a data filter for estimation of the DSGE model when the BVAR model is not identified. Moreover, we show how to compute the impulse responses to the same type of shock shared by the DSGE and BVAR models when the shock is identified in the BVAR model. Our exercises demonstrate the importance of integrating model uncertainty and parameter uncertainty to address potential model misspecification in macroeconomics.

Normalization, probability distribution, and impulse responses

When impulse responses in dynamic multivariate models such as identified VARs are given economic interpretations, it is important that reliable statistical inferences be provided. Before probability assessments are provided, however, the model must be normalized. Contrary to the conventional wisdom, this paper argues that normalization, a rule of reversing signs of coefficients in equations in a particular way, could considerably affect the shape of the likelihood and thus probability bands for impulse responses. A new concept called ML distance normalization is introduced to avoid distorting the shape of the likelihood. Moreover, this paper develops a Monte Carlo simulation technique for implementing ML distance normalization.Econometric models ; Monetary policy

Microcanonical rates from ring-polymer molecular dynamics: Direct-shooting, stationary-phase, and maximum-entropy approaches

We address the calculation of microcanonical reaction rates for processes involving significant nuclear quantum effects using ring-polymer molecular dynamics (RPMD), both with and without electronically non-adiabatic transitions. After illustrating the shortcomings of the naive free-particle direct-shooting method, in which the temperature of the internal ring-polymer modes is set to the translational energy scale, we investigate alternative strategies based on the expression for the microcanonical rate in terms of the inverse Laplace transform of the thermal reaction rate. It is shown that simple application of the stationary-phase approximation (SPA) dramatically improves the performance of the microcanonical rates using RPMD, particularly in the low-energy region where tunneling dominates. Using the SPA as a Bayesian prior, numerically exact RPMD microcanonical rates are then obtained using maximum entropy inversion of the thermal reaction rates for both electronically adiabatic and non-adiabatic model systems. Finally, the direct-shooting method is revisited using the SPA-determined temperature for the internal ring-polymer modes, leading to a simple, direct-simulation method with improved accuracy in the tunneling regime. This work suggests a general strategy for the extraction of microcanonical dynamical quantities from RPMD (or other approximate thermal) simulations

Improved transfer matrix method without numerical instability

A new improved transfer matrix method (TMM) is presented. It is shown that the method not only overcomes the numerical instability found in the original TMM, but also greatly improves the scalability of computation. The new improved TMM has no extra cost of computing time as the length of homogeneous scattering region becomes large. The comparison between the scattering matrix method(SMM) and our new TMM is given. It clearly shows that our new method is much faster than SMM.Comment: 5 pages,3 figure