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Kim, J. S., Reuter, J., Rolbiecki, K., & Ruiz de Austri, R. (2016). A resonance without resonance: Scrutinizing the diphoton excess at 750 GeV. Phys. Lett. B, 755, 403–408.
Abstract: Motivated by the recent diphoton excesses reported by both ATLAS and CMS collaborations, we suggest that a new heavy spinless particle is produced in gluon fusion at the LHC and decays to a couple of lighter pseudoscalars which then decay to photons. The new resonances could arise from a new strongly interacting sector and couple to Standard Model gauge bosons only via the corresponding Wess-Zumino-Witten anomaly. We present a detailed recast of the newest 13 TeV data from ATLAS and CMS together with the 8 TeV data to scan the consistency of the parameter space for those resonances.
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Otten, S., Rolbiecki, K., Caron, S., Kim, J. S., Ruiz de Austri, R., & Tattersall, J. (2020). DeepXS: fast approximation of MSSM electroweak cross sections at NLO. Eur. Phys. J. C, 80(1), 12–9pp.
Abstract: We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for pp ->(chi) over tilde (+)(1)(chi) over tilde (-)(1), (chi) over tilde (0)(2)(chi) over tilde (0)(2) and (chi) over tilde (0)(2)(chi) over tilde (+/-)(1) as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below 0.5% allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of O(10(7)) from O(min) to O(mu s) per evaluation.
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Flores, M. M., Kim, J. S., Rolbiecki, K., & Ruiz de Austri, R. (2023). Updated LHC bounds on MUED after run 2. Int. J. Mod. Phys. A, 38(1), 2350002–14pp.
Abstract: We present updated LHC limits on the minimal universal extra dimensions (MUEDs) model from the Run 2 searches. We scan the parameter space against a number of searches implemented in the public code CheckMATE and derive up-to-date limits on the MUED parameter space from 13TeV searches. The strongest constraints come from a search dedicated to squarks and gluinos with one isolated lepton, jets and missing transverse energy. In the procedure, we take into account initial state radiation and stress its importance in the MUED searches, which is not always appreciated.
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Desai, N., Domingo, F., Kim, J. S., Ruiz de Austri, R., Rolbiecki, K., Sonawane, M., et al. (2021). Constraining electroweak and strongly charged long-lived particles with CheckMATE. Eur. Phys. J. C, 81(11), 968–19pp.
Abstract: Long-lived particles have become a new frontier in the exploration of physics beyond the Standard Model. In this paper, we present the implementation of four types of long-lived particle searches, viz. displaced leptons, disappearing track, displaced vertex with either muons or with missing transverse energy, and heavy charged tracks. These four categories cover the signatures of a large range of physics models. We illustrate their potential for exclusion and discuss their mutual overlaps in mass-lifetime space for two simple phenomenological models involving either a U(1)-charged or a coloured scalar.
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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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