Liptak, Z. et al, & Marinas, C. (2022). Measurements of beam backgrounds in SuperKEKB Phase 2. Nucl. Instrum. Methods Phys. Res. A, 1040, 167168–19pp.
Abstract: The high design luminosity of the SuperKEKB electron–positron collider will result in challenging levels of beam-induced backgrounds in the interaction region. Understanding and mitigating these backgrounds is critical to the success of the Belle II experiment. We report on the first background measurements performed after roll-in of the Belle II detector, a period known as SuperKEKB Phase 2, utilizing both the BEAST II system of dedicated background detectors and the Belle II detector itself. We also report on first revisions to the background simulation made in response to our findings. Backgrounds measured include contributions from synchrotron radiation, beam-gas, Touschek, and injection backgrounds. At the end of Phase 2, single-beam backgrounds originating from the 4 GeV positron Low Energy Ring (LER) agree reasonably well with simulation, while backgrounds from the 7 GeV electron High Energy Ring (HER) are approximately one order of magnitude higher than simulation. We extrapolate these backgrounds forward and conclude it is safe to install the Belle II vertex detector.
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Middeldorf-Wygas, M. M., Oldengott, I. M., Bödeker, D., & Schwarz, D. J. (2022). Cosmic QCD transition for large lepton flavor asymmetries. Phys. Rev. D, 105, 123533–10pp.
Abstract: We study the impact of large lepton flavor asymmetries on the cosmic QCD transition. Scenarios of unequal lepton flavor asymmetries are observationally almost unconstrained and therefore open up a whole new parameter space for the cosmic QCD transition. We find that for large asymmetries, the formation of a Bose-Einstein condensate of pions can occur and identify the corresponding parameter space. In the vicinity of the QCD transition scale, we express the pressure in terms of a Taylor expansion with respect to the complete set of chemical potentials. The Taylor coefficients rely on input from lattice QCD calculations from the literature. The domain of applicability of this method is discussed.
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NA64 Collaboration(Andreev, Y. M. et al), & Molina Bueno, L. (2022). Search for a New B-L Z' Gauge Boson with the NA64 Experiment at CERN. Phys. Rev. Lett., 129, 161801–6pp.
Abstract: A search for a new Z′ gauge boson associated with (un)broken B−L symmetry in the keV–GeV mass range is carried out for the first time using the missing-energy technique in the NA64 experiment at the CERN SPS. From the analysis of the data with 3.22×10^11 electrons on target collected during 2016–2021 runs, no signal events were found. This allows us to derive new constraints on the Z′−e coupling strength, which, for the mass range 0.3≲ mZ′≲ 100 MeV, are more stringent compared to those obtained from the neutrino-electron scattering data.
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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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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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