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Chala, M., Delgado, A., Nardini, G., & Quiros, M. (2017). A light sneutrino rescues the light stop. J. High Energy Phys., 04(4), 097–22pp.
Abstract: Stop searches in supersymmetric frameworks with R-parity conservation usually assume the lightest neutralino to be the lightest supersymmetric particle. In this paper we consider an alternative scenario in which the left-handed tau sneutrino is lighter than neutralinos and stable at collider scales, but possibly unstable at cosmological scales. Moreover the (mostly right-handed) stop (t) over tilde is lighter than all electroweakinos, and heavier than the scalars of the third generation lepton doublet, whose charged component, (T) over tilde, is heavier than the neutral one, (v) over tilde. The remaining supersymmetric particles are decoupled from the stop phenomenology. In most of the parameter space, the relevant stop decays are only into t (T) over tildeT, t (v) over tildev and b (v) over tildeT via off-shell electroweakinos. We constrain the branching ratios of these decays by recasting the most sensitive stop searches. Due to the “double invisible” kinematics of the (t) over tilde -> t (v) over tildev process, and the low efficiency in tagging the t (T) over tildeT decay products, light stops are generically allowed. In the minimal supersymmetric standard model with similar to 100 GeV sneutrinos, stops with masses as small as similar to 350 GeV turn out to be allowed at 95% CL.
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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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Hirsch, M., Krauss, M. E., Opferkuch, T., Porod, W., & Staub, F. (2016). A constrained supersymmetric left-right model. J. High Energy Phys., 03(3), 009–22pp.
Abstract: We present a supersymmetric left-right model which predicts gauge coupling unification close to the string scale and extra vector bosons at the TeV scale. The subtleties in constructing a model which is in agreement with the measured quark masses and mixing for such a low left-right breaking scale are discussed. It is shown that in the constrained version of this model radiative breaking of the gauge symmetries is possible and a SM-like Higgs is obtained. Additional CP-even scalars of a similar mass or even much lighter are possible. The expected mass hierarchies for the supersymmetric states differ clearly from those of the constrained MSSM. In particular, the lightest down-type squark, which is a mixture of the sbottom and extra vector-like states, is always lighter than the stop. We also comment on the model's capability to explain current anomalies observed at the LHC.
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Balazs, C. et al, Mamuzic, J., & Ruiz de Austri, R. (2021). A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications. J. High Energy Phys., 05(5), 108–46pp.
Abstract: Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.
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