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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Kim, J. S., Rolbiecki, K., & Ruiz de Austri, R. (2016). Model-independent combination of diphoton constraints at 750 GeV. Eur. Phys. J. C, 76(5), 251–8pp.
Abstract: Motivated by the recent diphoton excess reported by both the ATLAS and CMS collaborations, we provide a model-independent combination of diphoton results obtained at root s = 8 and 13 TeV at the LHC. We consider resonant s-channel production of a spin-0 and spin-2 particle with a mass of 750 GeV that subsequently decays to two photons. The size of the excess reported by ATLAS appears to be in a slight tension with other measurements under the spin-2 particle hypothesis.
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De Romeri, V., Kim, J. S., Martin Lozano, V., Rolbiecki, K., & Ruiz de Austri, R. (2016). Confronting dark matter with the diphoton excess from a parent resonance decay. Eur. Phys. J. C, 76(5), 262–13pp.
Abstract: A diphoton excess with an invariant mass of about 750 GeV has been recently reported by both ATLAS and CMS experiments at LHC. While the simplest interpretation requires the resonant production of a 750 GeV (pseudo) scalar, here we consider an alternative setup, with an additional heavy parent particle which decays into a pair of 750 GeV resonances. This configuration improves the agreement between the 8 and 13 TeV data. Moreover, we include a dark matter candidate in the form of a Majorana fermion which interacts through the 750 GeV portal. The invisible decays of the light resonance help to suppress additional decay channels into Standard Model particles in association with the diphoton signal. We realise our hierarchical framework in the context of an effective theory, and we analyse the diphoton signal as well as the consistency with other LHC searches. We finally address the interplay of the LHC results with the dark matter phenomenology, namely the compatibility with the relic density abundance and the indirect detection bounds.
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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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Begone, G., Deisenroth, M. P., Kim, J. S., Liem, S., Ruiz de Austri, R., & Welling, M. (2019). Accelerating the BSM interpretation of LHC data with machine learning. Phys. Dark Universe, 24, 100293–5pp.
Abstract: The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC.
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