ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo, F. L., et al. (2021). Search for Dark Matter Produced in Association with a Dark Higgs Boson Decaying into (WW -/+)-W-+/- or ZZ in Fully Hadronic Final States from root s=13 TeV pp Collisions Recorded with the ATLAS Detector. Phys. Rev. Lett., 126(12), 121802–21pp.
Abstract: Several extensions of the Standard Model predict the production of dark matter particles at the LHC. An uncharted signature of dark matter particles produced in association with VV = (WW -/+)-W-+/- or ZZ pairs from a decay of a dark Higgs boson s is searched for using 139 fb(-1) of pp collisions recorded by the ATLAS detector at a center-of-mass energy of 13 TeV. The s -> V(q (q) over bar )V(q (q) over bar) decays are reconstructed with a novel technique aimed at resolving the dense topology from boosted VV pairs using jets in the calorimeter and tracking information. Dark Higgs scenarios with m(s) > 160 GeV are excluded.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2021). Longitudinal Flow Decorrelations in Xe plus Xe Collisions at root s(NN )=5.44 TeV with the ATLAS Detector. Phys. Rev. Lett., 126(12), 122301–20pp.
Abstract: The first measurement of longitudinal decorrelations of harmonic flow amplitudes v(n) for n = 2-4 in Xe + Xe collisions at root s(NN) = 5.44 TeV is obtained using 3 μb(-1) of data with the ATLAS detector at the LHC. The decorrelation signal for v(3) and v(4) is found to be nearly independent of collision centrality and transverse momentum (p(T)) requirements on fmal-state particles, but for v(2) a strong centrality and p(T) dependence is seen. When compared with the results from Pb + Pb collisions at. root s(NN) = 5.02 TcV, the longitudinal decorrelation signal in midcentral Xe + Xe collisions is found to be larger for v(2), but smaller for v(3). Current hydrodynamic models reproduce the ratios of the v(n) measured in Xe + Xe collisions to those in Pb + Pb collisions but fail to describe the magnitudes and trends of the ratios of longitudinal flow decorrelations between Xe + Xe and Pb + Pb. The results on the system-size dependence provide new insights and an important lever arm to separate effects of the longitudinal structure of the initial state from other early and late time effects in heavy-ion collisions.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo, F. L., et al. (2021). Search for Displaced Leptons in root s=13 TeV pp Collisions with the ATLAS Detector. Phys. Rev. Lett., 127(5), 051802–21pp.
Abstract: A search for charged leptons with large impact parameters using 139 fb(-1) of root s = 13 TeV pp collision data from the ATLAS detector at the LHC is presented, addressing a long-standing gap in coverage of possible new physics signatures. Results are consistent with the background prediction. This search provides unique sensitivity to long-lived scalar supersymmetric lepton partners (sleptons). For lifetimes of 0.1 ns, selectron, smuon, and stau masses up to 720, 680, and 340 GeV, respectively, are excluded at 95% confidence level, drastically improving on the previous best limits from LEP.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo, F. L., et al. (2021). Search for New Phenomena in Final States with Two Leptons and One or No b-Tagged Jets at root s=13 TeV Using the ATLAS Detector. Phys. Rev. Lett., 127(14), 141801–23pp.
Abstract: A search for new phenomena is presented in final states with two leptons and one or no b-tagged jets. The event selection requires the two leptons to have opposite charge, the same flavor (electrons or muons), and a large invariant mass. The analysis is based on the full run-2 proton-proton collision dataset recorded at a center-of-mass energy off root S = 13 TeV by the ATLAS experiment at the LHC, corresponding to an integrated luminosity of 139 fb(-1). No significant deviation from the expected background is observed in the data. Inspired by the B-meson decay anomalies, a four-fermion contact interaction between two quarks (b, s) and two leptons (ee or μmu) is used as a benchmark signal model, which is characterized by the energy scale and coupling, Lambda and g(*), respectively. Contact interactions with Lambda/g(*) lower than 2.0 (2.4) TeV are excluded for electrons (muons) at the 95% confidence level, still far below the value that is favored by the B-meson decay anomalies. Model-independent limits are set as a function of the minimum dilepton invariant mass, which allow the results to be reinterpreted in various signal scenarios.
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Conde, D., Castillo, F. L., Escobar, C., García, C., Garcia Navarro, J. E., Sanz, V., et al. (2023). Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning. Space Weather, 21(11), e2023SW003474–27pp.
Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.
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