|
|
van Beekveld, M., Beenakker, W., Caron, S., & Ruiz de Austri, R. (2016). The case for 100 GeV bino dark matter: a dedicated LHC tri-lepton search. J. High Energy Phys., 04(4), 154–26pp.
Abstract: Global fit studies performed in the pMSSM and the photon excess signal originating from the Galactic Center seem to suggest compressed electroweak supersymmetric spectra with a similar to 100 GeV bino-like dark matter particle. We find that these scenarios are not probed by traditional electroweak supersymmetry searches at the LHC. We propose to extend the ATLAS and CMS electroweak supersymmetry searches with an improved strategy for bino-like dark matter, focusing on chargino plus next-to-lightest neutralino production, with a subsequent decay into a tri-lepton final state. We explore the sensitivity for pMSSM scenarios with Delta m = m(NLSP) – m(LSF) similar to(5 – 50) GeV in the root s = 14 TeV run of the LHC. Counterintuitively, we find that the requirement of low missing transverse energy increases the sensitivity compared to the current ATLAS and CMS searches. With 300 fb(-1) of data we expect the LHC experiments to be able to discover these supersymmetric spectra with mass gaps down to Am 9 GeV for DM masses between 40 and 140 GeV. We stress the importance of a dedicated search strategy that targets precisely these favored pMSSM spectra.
|
|
|
|
van Beekveld, M., Caron, S., & Ruiz de Austri, R. (2020). The current status of fine-tuning in supersymmetry. J. High Energy Phys., 01(1), 147–41pp.
Abstract: In this paper, we minimize and compare two different fine-tuning measures in four high-scale supersymmetric models that are embedded in the MSSM. In addition, we determine the impact of current and future dark matter direct detection and collider experiments on the fine-tuning. We then compare the low-scale electroweak measure with the high-scale Barbieri-Giudice measure. We find that they reduce to the same value when the higgsino parameter drives the degree of fine-tuning. We also find spectra where the high-scale measure turns out to be lower than the low-scale measure. Depending on the high-scale model and fine-tuning definition, we find a minimal fine-tuning of 3-38 (corresponding to O(10-1)%) for the low-scale measure, and 63-571 (corresponding to O(1-0.1)%) for the high-scale measure. We stress that it is too early to conclude on the fate of supersymmetry, based only on the fine-tuning paradigm.
|
|
|
|
Esteves, J. N., Romao, J. C., Hirsch, M., Porod, W., Staub, F., & Vicente, A. (2012). Dark matter and LHC phenomenology in a left-right supersymmetric model. J. High Energy Phys., 01(1), 095–33pp.
Abstract: Left-right symmetric extensions of the Minimal Supersymmetric Standard Model can explain neutrino data and have potentially interesting phenomenology beyond that found in minimal SUSY seesaw models. Here we study a SUSY model in which the left-right symmetry is broken by triplets at a high scale, but significantly below the GUT scale. Sparticle spectra in this model differ from the usual constrained MSSM expectations and these changes affect the relic abundance of the lightest neutralino. We discuss changes for the standard stau (and stop) co-annihilation, the Higgs funnel and the focus point regions. The model has potentially large lepton flavour violation in both, left and right, scalar leptons and thus allows, in principle, also for flavoured co-annihilation. We also discuss lepton flavour signals due to violating decays of the second lightest neutralino at the LHC, which can be as large as 20 fb(-1) at root s = 14 TeV.
|
|
|
|
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.
|
|
|
|
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.
|
|
