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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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Navarro-Salas, J., & Pla, S. (2022). Particle Creation and the Schwinger Model. Symmetry-Basel, 14(11), 2435–9pp.
Abstract: We study the particle creation process in the Schwinger model coupled with an external classical source. One can approach the problem by taking advantage of the fact that the full quantized model is solvable and equivalent to a (massive) gauge field with a non-local effective action. Alternatively, one can also face the problem by following the standard semiclassical route. This means quantizing the massless Dirac field and considering the electromagnetic field as a classical background. We evaluate the energy created by a generic, homogeneous, and time-dependent source. The results match exactly in both approaches. This proves in a very direct and economical way the validity of the semiclassical approach for the (massless) Schwinger model, in agreement with a previous analysis based on the linear response equation. Our discussion suggests that a similar analysis for the massive Schwinger model could be used as a non-trivial laboratory to confront a fully quantized solvable model with its semiclassical approximation, therefore mimicking the long-standing confrontation of quantum gravity with quantum field theory in curved spacetime.
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Gariazzo, S., de Salas, P. F., Pisanti, O., & Consiglio, R. (2022). PArthENoPE revolutions. Comput. Phys. Commun., 271, 108205–13pp.
Abstract: This paper presents the main features of a new and updated version of the program PArthENoPE, which the community has been using for many years for computing the abundances of light elements produced during Big Bang Nucleosynthesis. This is the third release of the PArthENoPE code, after the 2008 and the 2018 ones, and will be distributed from the code's website, http://parthenope.na.infn.it. Apart from minor changes, the main improvements in this new version include a revisited implementation of the nuclear rates for the most important reactions of deuterium destruction, H-2(p,gamma) He-3, H-2(d, n)He-3 and H-2(d, p)H-3, and a re-designed GUI, which extends the functionality of the previous one. The new GUI, in particular, supersedes the previous tools for running over grids of parameters with a better management of parallel runs, and it offers a brand-new set of functions for plotting the results. Program summary Program title: PArthENoPE 3.0 CPC Library link to program files: https://doi.org/10.17632/wygr7d8yt9.2 Developer's repository link: http://parthenope.na.infn.it Licensing provisions: GPLv3 Programming language: Fortran 77 and Python Nature of problem: Computation of yields of light elements synthesized in the primordial universe Solution method: Livermore Solver for Ordinary Differential Equations (LSODE) for stiff and nonstiff systems, Python GUI for running and plotting Journal reference of previous version: Comput. Phys. Commun. 233 (2018) 237-242 Does the new version supersede the previous version?: Yes Reasons for the new version: Update of the physics and improvements in the GUI Summary of revisions: Update of the physics implemented in the Fortran code and improvements in the GUI functionalities, in particular new plotting functions.
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Belle-II DEPFET and PXD Collaborations(Wang, B. et al), & Marinas, C. (2022). Operational experience of the Belle II pixel detector. Nucl. Instrum. Methods Phys. Res. A, 1032, 166631–7pp.
Abstract: The Belle II experiment at the SuperKEKB accelerator has started its physics data taking with the full detector setup in March 2019. It aims to collect 40 times more e+e- collision data compared with its predecessor Belle experiment. The Belle II pixel detector (PXD) is based on the Depleted P-channel Field Effect Transistor (DEPFET) technology. The PXD plays an important role in the tracking and vertexing of the Belle II detector. Its two layers are arranged at radii of 14 mm and 22 mm around the interaction point. The sensors are thinned down to 75 μm to minimize multiple scattering, and each module has interconnects and ASICs integrated on the sensor with silicon frames for mechanical support. PXD showed good performance during data taking. It also faces several operational challenges due to the high background level from the SuperKEKB accelerator, such as the damage from beam loss events, the drift in the HV working point due to radiation effect, and the impact of the high background.
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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., et al. (2022). Operation and performance of the ATLAS semiconductor tracker in LHC Run 2. J. Instrum., 17(1), P01013–56pp.
Abstract: The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules. During Run 2 (2015-2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb(-1) to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector. Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2. It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%. Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules. '
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