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LHCb Collaboration(Aaij, R. et al), Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., et al. (2022). Precise determination of the B-s(0)-B-s(-0) oscillation frequency. Nat. Phys., 18, 54–58.
Abstract: Mesons comprising a beauty quark and strange quark can oscillate between particle (B-s(0)) and antiparticle (B-s(-0)) flavour eigenstates, with a frequency given by the mass difference between heavy and light mass eigenstates, Delta m(s). Here we present a measurement of Delta m(s) using B-s(0) -> D-s(-)pi(+) decays produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The oscillation frequency is found to be Delta m(s) = 17.7683 +/- 0.0051 +/- 0.0032 ps(-1), where the first uncertainty is statistical and the second is systematic. This measurement improves on the current Delta m(s) precision by a factor of two. We combine this result with previous LHCb measurements to determine Delta m(s) = 17.7656 +/- 0.0057 ps(-1), which is the legacy measurement of the original LHCb detector.
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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. (2022). Performance of the ATLAS Level-1 topological trigger in Run 2. Eur. Phys. J. C, 82(1), 7–26pp.
Abstract: During LHC Run 2 (2015-2018) the ATLAS Level-1 topological trigger allowed efficient data-taking by the ATLAS experiment at luminosities up to 2.1 x10(34) cm(-2) s(-1), which exceeds the design value by a factor of two. The system was installed in 2016 and operated in 2017 and 2018. It uses Field Programmable Gate Array processors to select interesting events by placing kinematic and angular requirements on electromagnetic clusters, jets, tau-leptons, muons and the missing transverse energy. It allowed to significantly improve the background event rejection and signal event acceptance, in particular for Higgs and B-physics processes.
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Roca, L., Liang, W. H., & Oset, E. (2022). Inconsistency of the data on the K-1(1270) -> pi K-0*(1430) decay width. Phys. Lett. B, 824, 136827–3pp.
Abstract: We show, using the same Lagrangian for the K-1(1270) -> pi K-0*(1430) and K-0*(1430) -> K-1 (1270)pi decays, that the present PDG data on the partial decay width of K-1 (1270) -> pi K-0*(1430) implies a width for K-0*(1430) -> K-1 (1270)pi decay which is about one order of magnitude larger than the total K-0*(1430) width. A discussion on this inconsistency is done, stressing its relationship to the existence of two K-1(1270) states obtained with the chiral unitary theory, which are not considered in the experimental analyses of K pi pi data.
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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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Morales, A. I., & Tuzon, P. (2022). Misconceptions, Knowledge, and Attitudes Towards the Phenomenon of Radioactivity. Sci. Educ., 31, 405–426.
Abstract: The teaching of the phenomenon of radioactivity is considered a key ingredient in the path towards developing critical thinking skills in many secondary science education curricula. Despite being one of the basic concepts in general physics courses, the scientific teaching literature of the last 40 years reports a great deal of misconceptions and conceptual errors related to radioactivity that seemingly appear regardless of the educational level and context. This study reports the first cross-sectional diagnostic study in Spain to secondary education students and pre-service teachers. Data were collected in the year 2019 through a questionnaire adapted from a previously validated one to explore the main misconceptions, attitudes, and knowledge status on the topic on a sample of 191 secondary school students and 29 Physics-and-Chemistry trainee teachers in the Spanish region of Valencia. Open and closed questions were used to categorize the entity itself, its properties, and the main misconceptions related to radioactivity. The responses were analysed using conventional statistical methods. The results indicate an evolution from a widespread dissenting notion on the phenomenon, which is staunchly related to danger, hazard, and destruction in the lowest educational levels, towards a more rational, relative, and multidimensional perspective in the highest ones. On the other hand, the ideas, emotions, and attitudes of the inquired individuals are in good agreement with the main misconceptions reported in the literature.
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Herrero-Garcia, J., Patrick, R., & Scaffidi, A. (2022). A semi-supervised approach to dark matter searches in direct detection data with machine learning. J. Cosmol. Astropart. Phys., 02, 039–19pp.
Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.
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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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