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van Beekveld, M., Beenakker, W., Caron, S., Kip, J., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Non-standard neutrino spectra from annihilating neutralino dark matter. SciPost Phys. Core, 6(1), 006–23pp.
Abstract: Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation.
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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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Dorigo, T. et al, Ramos, A., & Ruiz de Austri, R. (2023). Toward the end-to-end optimization of particle physics instruments with differentiable programming. Rev. Phys., 10, 100085– pp.
Abstract: The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, “experience-driven” layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.
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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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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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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., Mamuzic, J., Mitsou, V. A., Ruiz de Austri, R., et al. (2018). Search for magnetic monopoles with the MoEDAL forward trapping detector in 2.11 fb(-1) of 13 TeV proton-proton collisions at the LHC. Phys. Lett. B, 782, 510–516.
Abstract: We update our previous search for trapped magnetic monopoles in LHC Run 2 using nearly six times more integrated luminosity and including additional models for the interpretation of the data. The MoEDAL forward trapping detector, comprising 222 kg of aluminium samples, was exposed to 2.11 fb(-1) of 13 TeV proton-proton collisions near the LHCb interaction point and analysed by searching for induced persistent currents after passage through a superconducting magnetometer. Magnetic charges equal to the Dirac charge or above are excluded in all samples. The results are interpreted in Drell-Yan production models for monopoles with spins 0, 1/2 and 1: in addition to standard point-like couplings, we also consider couplings with momentum-dependent form factors. The search provides the best current laboratory constraints for monopoles with magnetic charges ranging from two to five times the Dirac charge.
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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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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Mamuzic, J., Mitsou, V. A., Papavassiliou, J., Ruiz de Austri, R., et al. (2019). Magnetic Monopole Search with the Full MoEDAL Trapping Detector in 13 TeV pp Collisions Interpreted in Photon-Fusion and Drell-Yan Production. Phys. Rev. Lett., 123(2), 021802–7pp.
Abstract: MoEDAL is designed to identify new physics in the form of stable or pseudostable highly ionizing particles produced in high-energy Large Hadron Collider (LHC) collisions. Here we update our previous search for magnetic monopoles in Run 2 using the full trapping detector with almost four times more material and almost twice more integrated luminosity. For the first time at the LHC, the data were interpreted in terms of photon-fusion monopole direct production in addition to the Drell-Yan-like mechanism. The MoEDAL trapping detector, consisting of 794 kg of aluminum samples installed in the forward and lateral regions, was exposed to 4.0 fb(-1) of 13 TeV proton-proton collisions at the LHCb interaction point and analyzed by searching for induced persistent currents after passage through a superconducting magnetometer. Magnetic charges equal to or above the Dirac charge are excluded in all samples. Monopole spins 0, 1/2, and 1 are considered and both velocity-independent and-dependent couplings are assumed. This search provides the best current laboratory constraints for monopoles with magnetic charges ranging from two to five times the Dirac charge.
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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Mamuzic, J., Mitsou, V. A., Papavassiliou, J., Ruiz de Austri, R., et al. (2021). First Search for Dyons with the Full MoEDAL Trapping Detector in 13 TeV pp Collisions. Phys. Rev. Lett., 126(7), 071801–7pp.
Abstract: The MoEDAL trapping detector consists of approximately 800 kg of aluminum volumes. It was exposed during run 2 of the LHC program to 6.46 fb(-1) of 13 TeV proton-proton collisions at the LHCb interaction point. Evidence for dyons (particles with electric and magnetic charge) captured in the trapping detector was sought by passing the aluminum volumes comprising the detector through a superconducting quantum interference device (SQUID) magnetometer. The presence of a trapped dyon would be signaled by a persistent current induced in the SQUID magnetometer. On the basis of a Drell-Yan production model, we exclude dyons with a magnetic charge ranging up to five Dirac charges (5g(D)) and an electric charge up to 200 times the fundamental electric charge for mass limits in the range 870-3120 GeV and also monopoles with magnetic charge up to and including 5g(D) with mass limits in the range 870-2040 GeV.
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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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