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At least 1,351 records · Page 75

Study of the rare decays of B s 0 and B 0 mesons into muon pairs using data collected during 2015 and 2016 with the ATLAS detector

A study of the decays B s 0 → μ + μ – and B 0 → μ + μ – has been performed using 26.3 fb –1 of 13 TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolution in μ + μ – invariant mass is comparable to the B s 0 -B 0 mass difference, a single fit determines the signal yields for both decay modes. This results in a measurement of the branching fraction B(B s 0 → μ + μ – ) = (3.2 +1.1 –1.0 ) × 10 –9 and an upper limit B(B 0 → μ + μ – ) < 4.3×10 –10 at 95% confidence level. The result is combined with the Run 1 ATLAS result, yielding B(B s 0 → μ + μ – ) = (2.8 +0.8 –0.7 ) × 10 –9 and B(B 0 → μ + μ – ) < 2.1 × 10 –10 at 95% confidence level. The combined result is consistent with the Standard Model prediction within 2.4 standard deviations in the B(B 0 → μ + μ – ) – B(B 0 s → μ + μ – ) plane.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of the charge asymmetry in dileptonic decays of top quark pairs in pp collisions at $ \sqrt{s}=7 $ TeV using the ATLAS detector

A measurement of the top-antitop (t$\bar{t}$) charge asymmetry is presented using data corresponding to an integrated luminosity of 4.6 fb -1 of LHC pp collisions at a centreof-mass energy of 7 TeV collected by the ATLAS detector. Events with two charged leptons, at least two jets and large missing transverse momentum are selected. Two observables are studied: A C ℓℓ based on the identified charged leptons, and A$t\bar{t}\atop{C}$, based on the reconstructed tt final state. The asymmetries are measured to be A $ℓℓ\atop{C}$ =0.024 ± 0.015 (stat.) ± 0.009 (syst.), A $t\bar{t}\atop{C}$ = 0.021 ± 0.025 (stat.) ± 0.017 (syst.). The measured values are in agreement with the Standard Model predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Examining the Effects of Chronic Selenium Exposure on Traditionally Used Stress Parameters in Juvenile American Alligators ( Alligator mississippiensis )

Environmental contaminants, such as the trace element selenium (Se), are a continuing concern to species worldwide due to their potential pathophysiological effects, including their influence on the stress response mediated through glucocorticoids (GCs; stress hormones). Environmental concentrations of Se are increasing due to anthropogenic activities, including the incomplete combustion of coal and subsequent disposal of coal combustion wastes. However, most studies examining how Se affects GCs have been focused on lower trophic organisms. The objectives of this study were to investigate the effects of long-term Se exposure on traditionally used stress parameters and to identify which of these parameters best indicate Se accumulation in liver and kidney of the American alligator (Alligator mississippiensis), a top trophic carnivore found in the southeastern United States and known to inhabit Se-containing areas. Alligators were divided into three dietary treatments and fed prey spiked with 1000 or 2000 ppm selenomethionine (SeMet) or deionized water (control treatment) for 7 weeks. Following the 7-week treatment protocol, blood and tissue samples were obtained to measure plasma corticosterone (CORT; the main crocodilian GC), tail scute CORT, the ratio of peripheral blood heterophils (H) to lymphocytes (L) as H/L ratio, and body condition. To evaluate which parameter best indicated Se accumulation in the liver and kidney, principal component and discriminant analyses were performed. The only parameter significantly correlated with liver and kidney Se concentrations was scute CORT. Furthermore, our results suggest that measurement of CORT in tail scutes, when compared to plasma CORT, H/L ratios, and body condition, is the best indicator of Se-exposure and accumulation in crocodilians.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Quantitative concentration measurements in a turbulent helium jet using rainbow schlieren deflectometry

Rainbow schlieren deflectometry (RSD) is applied to acquire quantitative concentration measurements in a turbulent environment for the first time. RSD methodology is developed and validated using Rayleigh scattering data available in the literature. Experiments were performed in an initially laminar, momentum-driven helium jet injected into ambient air. Full-field measurements are acquired that span the laminar, transition, and fully turbulent regions of the jet. Furthermore, the RSD results are shown to provide accurate measurements in each region and also confirm the self-similar nature of the fully turbulent jet. Analysis shows that the transition from laminar to turbulent flow occurs across an axial span that decreases with increasing Reynolds number. Experiments with a long exposure time were conducted to evaluate the limitations of hardware averaging of the RSD signal in comparison with software averaging.

02 PETROLEUM↗

Retrospective neutron spectrum determination of a (30 MeV D, Be) source using the multi-foil activation technique and STAYSL-PNNL [Characterization of neutron spectra from a (30 MeV d, Be) source using the multi-foil activation technique]

Retrospective characterization of a (30 MeV D, Be) neutron source was performed employing multi-foil activation and STAYSL-PNNL. Experimental reaction rates were calculated from gamma spectroscopy measurements of irradiated foils and MCNP provided the guess spectrum. Adjusted spectra were evaluated through activation calculations for a stainless-steel target using FISPACT-II. Adjusted spectra showed limited dependence on the dosimetry reactions and provided minor improvements in activation calculations. Omitting reflected neutrons in the guess spectrum generated poor activation results and the limited number of dosimetry reactions introduced doubt in the adjusted spectra. Furthermore, a dedicated neutron spectrometry experiment and a more detailed simulation is required.

42 ENGINEERING↗

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗