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Feroz, F., Cranmer, K., Hobson, M., Ruiz de Austri, R., & Trotta, R. (2011). Challenges of profile likelihood evaluation in multi-dimensional SUSY scans. J. High Energy Phys., 06(6), 042–23pp.
Abstract: Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MULTINEST, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration previously used in the literarture is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MULTINEST configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MULTINEST is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded.
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Hirsch, M., Reichert, L., & Porod, W. (2011). Supersymmetric mass spectra and the seesaw scale. J. High Energy Phys., 05(5), 086–32pp.
Abstract: Supersymmetric mass spectra within two variants of the seesaw mechanism, commonly known as type-II and type-III seesaw, are calculated using full 2-loop RGEs and minimal Supergravity boundary conditions. The type-II seesaw is realized using one pair of 15 and (15) over bar superfields, while the type-III is realized using three copies of 24(M) superfields. Using published, estimated errors on SUSY mass observables attainable at the LHC and in a combined LHC+ILC analysis, we calculate expected errors for the parameters of the models, most notably the seesaw scale. If SUSY particles are within the reach of the ILC, pure mSugra can be distinguished from mSugra plus type-II or type-III seesaw for nearly all relevant values of the seesaw scale. Even in the case when only the much less accurate LHC measurements are used, we find that indications for the seesaw can be found in favourable parts of the parameter space. Since our conclusions crucially depend on the reliability of the theoretically forecasted error bars, we discuss in some detail the accuracies which need to be achieved for the most important LHC and ILC observables before an analysis, such as the one presented here, can find any hints for type-II or type-III seesaw in SUSY spectra.
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Olmo, G. J. (2011). Palatini approach to modified gravity: f(R) theories and beyond. Int. J. Mod. Phys. D, 20(4), 413–462.
Abstract: We review the recent literature on modified theories of gravity in the Palatini approach. After discussing the motivations that lead to consider alternatives to Einstein's theory and to treat the metric and the connection as independent objects, we review several topics that have been recently studied within this framework. In particular, we provide an in-depth analysis of the cosmic speed-up problem, laboratory and solar system tests, the structure of stellar objects, the Cauchy problem, and bouncing cosmologies. We also discuss the importance of going beyond the f(R) models to capture other phenomenological aspects related with dark matter/energy and quantum gravity.
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Bridges, M., Cranmer, K., Feroz, F., Hobson, M., Ruiz de Austri, R., & Trotta, R. (2011). A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques. J. High Energy Phys., 03(3), 012–23pp.
Abstract: We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mapping between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of similar to 10(4) with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and identify the origin of this effect to physical boundaries in the parameter space. Finally, we point out that the effects intrinsic to the statistical procedure are conflated with simplifications to the likelihood functions from the experiments themselves.
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Esteves, J. N., Romao, J. C., Hirsch, M., Vicente, A., Porod, W., & Staub, F. (2010). LHC and lepton flavour violation phenomenology of a left-right extension of the MSSM. J. High Energy Phys., 12(12), 077–44pp.
Abstract: We study the phenomenology of a supersymmetric left-right model, assuming minimal supergravity boundary conditions. Both left-right and (B-L) symmetries are broken at an energy scale close to, but significantly below the GUT scale. Neutrino data is explained via a seesaw mechanism. We calculate the RGEs for superpotential and soft parameters complete at 2-loop order. At low energies lepton flavour violation (LFV) and small, but potentially measurable mass splittings in the charged scalar lepton sector appear, due to the RGE running. Different from the supersymmetric “pure seesaw” models, both, LFV and slepton mass splittings, occur not only in the left-but also in the right slepton sector. Especially, ratios of LFV slepton decays, such as Br((tau) over bar (R) -> μchi(0)(1))/Br((tau) over bar (L) -> μchi(0)(1)) are sensitive to the ratio of (B-L) and left-right symmetry breaking scales. Also the model predicts a polarization asymmetry of the outgoing positrons in the decay mu(+) -> e(+)gamma, A similar to [0, 1], which differs from the pure seesaw “prediction” A = 1. Observation of any of these signals allows to distinguish this model from any of the three standard, pure (mSugra) seesaw setups.
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