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Aarrestad, T. et al, Mamuzic, J., & Ruiz de Austri, R. (2022). Benchmark data and model independent event classification for the large hadron collider. SciPost Phys., 12(1), 043–57pp.
Abstract: We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
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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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van Beekveld, M., Beenakker, W., Caron, S., Peeters, R., & Ruiz de Austri, R. (2017). Supersymmetry with dark matter is still natural. Phys. Rev. D, 96(3), 035015–7pp.
Abstract: We identify the parameter regions of the phenomenological minimal supersymmetric standard model (pMSSM) with the minimal possible fine-tuning. We show that the fine-tuning of the pMSSM is not large, nor under pressure by LHC searches. Low sbottom, stop and gluino masses turn out to be less relevant for low fine-tuning than commonly assumed. We show a link between low fine-tuning and the dark matter relic density. Fine-tuning arguments point to models with a dark matter candidate yielding the correct dark matter relic density: a bino-higgsino particle with a mass of 35-155 GeV. Some of these candidates are compatible with recent hints seen in astrophysics experiments such as Fermi-LAT and AMS-02. We argue that upcoming direct search experiments, such as XENON1T, will test all of the most natural solutions in the next few years due to the sensitivity of these experiments on the spin-dependent WIMP-nucleon cross section.
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Lara, I., Lopez-Fogliani, D. E., Muñoz, C., Nagata, N., Otono, H., & Ruiz de Austri, R. (2018). Looking for the left sneutrino LSP with displaced-vertex searches. Phys. Rev. D, 98(7), 075004–17pp.
Abstract: We analyze a displaced dilepton signal expected at the LHC for a tau left sneutrino as the lightest supersymmetric particle with a mass in the range 45-100 GeV. The sneutrinos are pair produced via a virtual W, Z or gamma in the s channel and, given the large value of the tau Yukawa coupling, their decays into two dileptons or a dilepton plus missing transverse energy from neutrinos can be significant. The discussion is carried out in the framework of the μnu SSM, where the presence of R-parity violating couplings involving right-handed neutrinos solves the μproblem and can reproduce the neutrino data. To probe the tau left sneutrinos we compare the predictions of this scenario with the ATLAS search for long-lived particles using displaced lepton pairs in pp collisions at root s = 8 TeV, allowing us to constrain the parameter space of the model. We also consider an optimization of the trigger requirements used in existing displaced-vertex searches by means of a high level trigger that exploits tracker information. This optimization is generically useful for a light metastable particle decaying into soft charged leptons. The constraints on the sneutrino turn out to be more stringent. We finally discuss the prospects for the 13 TeV LHC searches as well as further potential optimizations.
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Domingo, F., Kim, J. S., Martin Lozano, V., Martin-Ramiro, P., & Ruiz de Austri, R. (2020). Confronting the neutralino and chargino sector of the NMSSM with the multilepton searches at the LHC. Phys. Rev. D, 101(7), 075010–29pp.
Abstract: We test the impact of the ATLAS and CMS multilepton searches performed at the LHC with 8 as well as 13 TeV center-of-mass energy (using only the pre-2018 results) on the chargino and neutralino sector of the next-to-minimal supersymmetric Standard Model (NMSSM). Our purpose consists in analyzing the actual reach of these searches for a full model and in emphasizing effects beyond the minimal supersymmetric Standard Model (MSSM) that affect the performance of current (MSSM-inspired) electroweakino searches. To this end, we consider several scenarios characterizing specific features of the NMSSM electroweakino sector. We then perform a detailed collider study, generating Monte Carlo events through PYTHIA and testing against current LHC constraints implemented in the public tool CheckMATE. We find e.g., that supersymmetric decay chains involving intermediate singlino or Higgs-singlet states can modify the naive MSSM-like picture of the constraints by inducing final states with softer or less easily identifiable SM particles-reversely, a compressed configuration with singlino next-to-lightest supersymmetric particle occasionally induces final states that are rich with photons, which could provide complementary search channels.
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Gomez, M. E., Lola, S., Ruiz de Austri, R., & Shafi, Q. (2018). Confronting SUSY GUT With Dark Matter, Sparticle Spectroscopy and Muon (g – 2). Front. Physics, 6, 127–9pp.
Abstract: We explore the implications of LHC and cold dark matter searches for supersymmetric particle mass spectra in two different grand unified models with left-right symmetry, SO(10) and SU(4)(c) x SU(2)(L) x SU(2)(R) (4-2-2). We identify characteristic differences between the two scenarios, which imply distinct correlations between experimental measurements and the particular structure of the GUT group. The gauge structure of 4-2-2 enhances significantly the allowed parameter space as compared to SO(10), giving rise to a variety of coannihilation scenarios compatible with the LHC data, LSP dark matter and the ongoing muon g-2 experiment.
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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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Otten, S., Caron, S., de Swart, W., van Beekveld, M., Hendriks, L., van Leeuwen, C., et al. (2021). Event generation and statistical sampling for physics with deep generative models and a density information buffer. Nat. Commun., 12(1), 2985–16pp.
Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
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Roszkowski, L., Ruiz de Austri, R., & Trotta, R. (2010). Efficient reconstruction of constrained MSSM parameters from LHC data: A case study. Phys. Rev. D, 82(5), 055003–12pp.
Abstract: We present an efficient method of reconstructing the parameters of the constrained MSSM from assumed future LHC data, applied both on their own right and in combination with the cosmological determination of the relic dark matter abundance. Focusing on the ATLAS SU3 benchmark point, we demonstrate that our simple Gaussian approximation can recover the values of its parameters remarkably well. We examine two popular noninformative priors and obtain very similar results, although when we use an informative, naturalness-motivated prior, we find some sizeable differences. We show that a further strong improvement in reconstructing the SU3 parameters can by achieved by applying additional information about the relic abundance at the level of WMAP accuracy, although the expected data from Planck will have only a very limited additional impact. Further external data may be required to break some remaining degeneracies. We argue that the method presented here is applicable to a wide class of low-energy effective supersymmetric models, as it does not require one to deal with purely experimental issues, e.g., detector performance, and has the additional advantages of computational efficiency. Furthermore, our approach allows one to distinguish the effect of the model's internal structure and of the external data on the final parameters constraints.
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Bertone, G., Cerdeño, D. G., Fornasa, M., Ruiz de Austri, R., & Trotta, R. (2010). Identification of dark matter particles with LHC and direct detection data. Phys. Rev. D, 82(5), 055008–7pp.
Abstract: Dark matter (DM) is currently searched for with a variety of detection strategies. Accelerator searches are particularly promising, but even if weakly interacting massive particles are found at the Large Hadron Collider (LHC), it will be difficult to prove that they constitute the bulk of the DM in the Universe Omega(DM). We show that a significantly better reconstruction of the DM properties can be obtained with a combined analysis of LHC and direct detection data, by making a simple Ansatz on the weakly interacting massive particles local density rho(0)((chi) over bar1), i.e., by assuming that the local density scales with the cosmological relic abundance, (rho(0)((chi) over bar1)/rho(DM)) = (Omega(0)((chi) over bar1)/Omega(DM)). We demonstrate this method in an explicit example in the context of a 24-parameter supersymmetric model, with a neutralino lightest supersymmetric particle in the stau coannihilation region. Our results show that future ton-scale direct detection experiments will allow to break degeneracies in the supersymmetric parameter space and achieve a significantly better reconstruction of the neutralino composition and its relic density than with LHC data alone.
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