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Cabrera, M. E., Casas, J. A., Delgado, A., Robles, S., & Ruiz de Austri, R. (2016). Naturalness of MSSM dark matter. J. High Energy Phys., 08(8), 058–30pp.
Abstract: There exists a vast literature examining the electroweak (EW) fine-tuning problem in supersymmetric scenarios, but little concerned with the dark matter (DM) one, which should be combined with the former. In this paper, we study this problem in an, as much as possible, exhaustive and rigorous way. We have considered the MSSM framework, assuming that the LSP is the lightest neutralino, chi(0)(1), and exploring the various possibilities for the mass and composition of chi(0)(1), as well as different mechanisms for annihilation of the DM particles in the early Universe (well-tempered neutralinos, funnels and co-annihilation scenarios). We also present a discussion about the statistical meaning of the fine-tuning and how it should be computed for the DM abundance, and combined with the EW fine-tuning. The results are very robust and model-independent and favour some scenarios (like the h-funnel when M-chi 10 is not too close to m(h)/2) with respect to others (such as the pure wino case). These features should be taken into account when one explores “natural SUSY” scenarios and their possible signatures at the LHC and in DM detection experiments.
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Caron, S., Kim, J. S., Rolbiecki, K., Ruiz de Austri, R., & Stienen, B. (2017). The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning. Eur. Phys. J. C, 77(4), 257–25pp.
Abstract: A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce theATLAS exclusion regions in 19 dimensions with an accuracy of at least 93%. It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/.
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Kim, J. S., Rolbiecki, K., Ruiz de Austri, R., Tattersall, J., & Weber, T. (2016). Prospects for natural SUSY. Phys. Rev. D, 94(9), 095013–19pp.
Abstract: As we anticipate the first results of the 2016 run, we assess the discovery potential of the LHC to “natural supersymmetry.” To begin with, we explore the region of the model parameter space that can be excluded with various center-of-mass energies (13 TeV and 14 TeV) and different luminosities (20 fb(-1), 100 fb(-1), 300 fb(-1) and 3000 fb(-1)). We find that the bounds at 95% C.L. on stops vary from m((t1) over tilde) greater than or similar to 800 GeV expected this summer to m((t1) over tilde) greater than or similar to 1500 GeV at the end of the high luminosity run, while gluino bounds are expected to range from m((g) over tilde) greater than or similar to 1700 GeV to m((g) over tilde) greater than or similar to 2500 GeV over the same time period. However, more pessimistically, we find that if no signal begins to appear this summer, only a very small region of parameter space can be discovered with 5 sigma significance. For this conclusion to change, we find that both theoretical and systematic uncertainties will need to be significantly reduced.
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Gomez-Vargas, G. A., Lopez-Fogliani, D. E., Muñoz, C., Perez, A. D., & Ruiz de Austri, R. (2017). Search for sharp and smooth spectral signatures of μnu SSM gravitino dark matter with Fermi- LAT. J. Cosmol. Astropart. Phys., 03(3), 047–23pp.
Abstract: The μnu SSM solves the μproblem of supersymmetric models and reproduces neutrino data, simply using couplings with right-handed neutrinos nu's. Given that these couplings break explicitly R parity, the gravitino is a natural candidate for decaying dark matter in the μnu SSM. In this work we carry out a complete analysis of the detection of μnu SSM gravitino dark matter through gamma-ray observations. In addition to the two-body decay producing a sharp line, we include in the analysis the three-body decays producing a smooth spectral signature. We perform first a deep exploration of the low-energy parameter space of the μnu SSM taking into account that neutrino data must be reproduced. Then, we compare the gamma-ray fluxes predicted by the model with Fermi-LAT observations. In particular, with the 95% CL upper limits on the total diffuse extragalactic gamma-ray background using 50 months of data, together with the upper limits on line emission from an updated analysis using 69.9 months of data. For standard values of bino and wino masses, gravitinos with masses larger than about 4 GeV, or lifetimes smaller than about 10(28) s, produce too large fluxes and are excluded as dark matter candidates. However, when limiting scenarios with large and close values of the gaugino masses are considered, the constraints turn out to be less stringent, excluding masses larger than 17 GeV and lifetimes smaller than 4 x 10(25) s.
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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., Mamuzic, J., Mitsou, V. A., Ruiz de Austri, R., et al. (2017). Search for Magnetic Monopoles with the MoEDAL Forward Trapping Detector in 13 TeV Proton-Proton Collisions at the LHC. Phys. Rev. Lett., 118(6), 061801–6pp.
Abstract: MoEDAL is designed to identify new physics in the form of long-lived highly ionizing particles produced in high-energy LHC collisions. Its arrays of plastic nuclear-track detectors and aluminium trapping volumes provide two independent passive detection techniques. We present here the results of a first search for magnetic monopole production in 13 TeV proton-proton collisions using the trapping technique, extending a previous publication with 8 TeV data during LHC Run 1. A total of 222 kg of MoEDAL trapping detector samples was exposed in the forward region and analyzed by searching for induced persistent currents after passage through a superconducting magnetometer. Magnetic charges exceeding half the Dirac charge are excluded in all samples and limits are placed for the first time on the production of magnetic monopoles in 13 TeV pp collisions. The search probes mass ranges previously inaccessible to collider experiments for up to five times the Dirac charge.
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