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.
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ATLAS Collaboration. (2022). A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery. Nature, 607(7917), 52–59.
Abstract: The standard model of particle physics(1-4) describes the known fundamental particles and forces that make up our Universe, with the exception of gravity. One of the central features of the standard model is a field that permeates all of space and interacts with fundamental particles(5-9). The quantum excitation of this field, known as the Higgs field, manifests itself as the Higgs boson, the only fundamental particle with no spin. In 2012, a particle with properties consistent with the Higgs boson of the standard model was observed by the ATLAS and CMS experiments at the Large Hadron Collider at CERN10,11. Since then, more than 30 times as many Higgs bosons have been recorded by the ATLAS experiment, enabling much more precise measurements and new tests of the theory. Here, on the basis of this larger dataset, we combine an unprecedented number of production and decay processes of the Higgs boson to scrutinize its interactions with elementary particles. Interactions with gluons, photons, and W and Z bosons-the carriers of the strong, electromagnetic and weak forces-are studied in detail. Interactions with three third-generation matter particles (bottom (b) and top (t) quarks, and tau leptons (tau)) are well measured and indications of interactions with a second-generation particle (muons, mu) are emerging. These tests reveal that the Higgs boson discovered ten years ago is remarkably consistent with the predictions of the theory and provide stringent constraints on many models of new phenomena beyond the standard model.
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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.
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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2023). Search for Rare Decays of D0 Mesons into Two Muons. Phys. Rev. Lett., 131(4), 041804–13pp.
Abstract: A search for the very rare D^{0}mu^{+}mu^{-} decay is performed using data collected by the LHCb experiment in proton-proton collisions at sqrt[s]=7, 8, and 13TeV, corresponding to an integrated luminosity of 9fb^{-1}. The search is optimized for D^{0} mesons from D^{*+}D^{0}pi^{+} decays but is also sensitive to D^{0} mesons from other sources. No evidence for an excess of events over the expected background is observed. An upper limit on the branching fraction of this decay is set at B(D^{0}mu^{+}mu^{-})<3.1*10^{-9} at a 90% C.L. This represents the world's most stringent limit, constraining models of physics beyond the standard model.
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Barberis, D. et al, Fernandez Casani, A., Garcia Montoro, C., Gonzalez de la Hoz, S., Salt, J., Sanchez, J., et al. (2023). The ATLAS EventIndex: A BigData Catalogue for All ATLAS Experiment Events. Comput. Softw. Big Sci., 7, 2–21pp.
Abstract: The ATLAS EventIndex system comprises the catalogue of all events collected, processed or generated by the ATLAS experiment at the CERN LHC accelerator, and all associated software tools to collect, store and query this information. ATLAS records several billion particle interactions every year of operation, processes them for analysis and generates even larger simulated data samples; a global catalogue is needed to keep track of the location of each event record and be able to search and retrieve specific events for in-depth investigations. Each EventIndex record includes summary information on the event itself and the pointers to the files containing the full event. Most components of the EventIndex system are implemented using BigData free and open-source software. This paper describes the architectural choices and their evolution in time, as well as the past, current and foreseen future implementations of all EventIndex components.
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