|
|
Garcia Navarro, J. E., Fernandez-Prieto, L. M., Villaseñor, A., Sanz, V., Ammirati, J. B., Diaz Suarez, E. A., et al. (2022). Performance of Deep Learning Pickers in Routine Network Processing Applications. Seismol. Res. Lett., 93, 2529–2542.
Abstract: Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures.
|
|
|
|
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.
|
|
|
|
ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., Cabrera Urban, S., et al. (2023). Measurement of Suppression of Large-Radius Jets and Its Dependence on Substructure in Pb+Pb Collisions at sqrt[s_{NN}]=5.02TeV with the ATLAS Detector. Phys. Rev. Lett., 131(17), 172301–22pp.
Abstract: This letter presents a measurement of the nuclear modification factor of large-radius jets in root sNN=5.02 TeV Pb+Pb collisions by the ATLAS experiment. The measurement is performed using 1.72nb^{-1} and 257pb^{-1} of Pb+Pb and pp data, respectively. The large-radius jets are reconstructed with the anti-k{t} algorithm using a radius parameter of R=1.0, by reclustering anti-k{t} R=0.2 jets, and are measured over the transverse momentum (p{T}) kinematic range of 158<p{T}<1000GeV and absolute pseudorapidity |y|<2.0. The large-radius jet constituents are further reclustered using the k{t} algorithm in order to obtain the splitting parameters, sqrt[d{12}] and DeltaR{12}, which characterize the transverse momentum scale and angular separation for the hardest splitting in the jet, respectively. The nuclear modification factor, R{AA}, obtained by comparing the Pb+Pb jet yields to those in pp collisions, is measured as a function of jet transverse momentum (p{T}) and sqrt[d{12}] or DeltaR{12}. A significant difference in the quenching of large-radius jets having single subjet and those with more complex substructure is observed. Systematic comparison of jet suppression in terms of R{AA} for different jet definitions is also provided. Presented results support the hypothesis that jets with hard internal splittings lose more energy through quenching and provide a new perspective for understanding the role of jet structure in jet suppression.
|
|
|
|
ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., Cabrera Urban, S., et al. (2023). A search for new resonances in multiple final states with a high transverse momentum Z boson in root s = 13 TeV pp collisions with the ATLAS detector. J. High Energy Phys., 06(6), 036–56pp.
Abstract: A generic search for resonances is performed with events containing a Z boson with transverse momentum greater than 100 GeV, decaying into e+e− or μ+μ−. The analysed data collected with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider correspond to an integrated luminosity of 139 fb−1. Two invariant mass distributions are examined for a localised excess relative to the expected Standard Model background in six independent event categories (and their inclusive sum) to increase the sensitivity. No significant excess is observed. Exclusion limits at 95% confidence level are derived for two cases: a model-independent interpretation of Gaussian-shaped resonances with the mass width between 3% and 10% of the resonance mass, and a specific heavy vector triplet model with the decay mode W′ → ZW → ℓℓqq.
|
|
|
|
ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2023). Search for lepton-flavour violation in high-mass dilepton final states using 139 fb−1 of pp collisions at root s= 13 TeV with the ATLAS detector. J. High Energy Phys., 10(10), 082–49pp.
Abstract: A search is performed for a heavy particle decaying into different-flavour, dilepton final states, using 139 fb−1 of proton-proton collision data at = 13 TeV collected in 2015–2018 by the ATLAS detector at the Large Hadron Collider. Final states with electrons, muons and hadronically decaying tau leptons are considered (eμ, eτ or μτ). No significant excess over the Standard Model predictions is observed. Upper limits on the production cross-section are set as a function of the mass of a Z′ boson, a supersymmetric τ-sneutrino, and a quantum black-hole. The observed 95% CL lower mass limits obtained on a typical benchmark model Z′ boson are 5.0 TeV (eμ), 4.0 TeV (eτ), and 3.9 TeV (μτ), respectively.
|
|
