On the evaluation of the Gottfried sum and d̄−ū asymmetry with the use of the TMM method

In this paper, we obtain the integrated flavor asymmetry of the sea quarks in the proton, [Formula: see text], with the help of the truncated moments approach elaborated in our previous papers. We use the difference between the light sea-quark distributions [Formula: see text] extracted from Drell–Yan (DY) NuSea/E866 measurements of the cross-section ratio [Formula: see text] and from the recent global analysis of deep inelastic scattering (DIS) [Formula: see text] data incorporating the reanalyzed neutron structure function. In our analysis, we also include the most recent DY data from the Fermilab SeaQuest/E906 experiment.

Production cross sections of tetraquark states in elementary hadronic collisions

Inclusive production cross sections of the possible exotic state X(3872) in proton–proton, pion-proton and proton–antiproton collisions are calculated using a statistical based model, which is previously used to describe inclusive charmed and bottomed hadron production cross sections in the low energy region. With the extensions made here the model is capable to include tetraquarks as well, using the diquark picture of tetraquarks. The evaluated cross section ratio of $$\varPsi (2S)$$ Ψ ( 2 S ) and X(3872) at $$\sqrt{s}=7$$ s = 7 TeV agrees well with the measured value.

Extracting bulk defect parameters in silicon wafers using machine learning models

The performance of high-efficiency silicon solar cells is limited by the presence of bulk defects. Identification of these defects has the potential to improve cell performance and reliability. The impact of bulk defects on minority carrier lifetime is commonly measured using temperature- and injection-dependent lifetime spectroscopy and the defect parameters, such as its energy level and capture cross-section ratio, are usually extracted by fitting the Shockley-Read-Hall equation. We propose an alternative extraction approach by using machine learning trained on more than a million simulated lifetime curves, achieving coefficient of determinations between the true and predicted values of the defect parameters above 99%. In particular, random forest regressors, show that defect energy levels can be predicted with a high precision of ±0.02 eV, 87% of the time. The traditional approach of fitting to the Shockley-Read-Hall equation usually yields two sets of defect parameters, one in each half bandgap. The machine learning model is trained to predict the half bandgap location of the energy level, and successfully overcome the traditional approach’s limitation. The proposed approach is validated using experimental measurements, where the machine learning predicts defect energy level and capture cross-section ratio within the uncertainty range of the traditional fitting method. The successful application of machine learning in the context of bulk defect parameter extraction paves the way to more complex data-driven physical models which have the potential to overcome the limitation of traditional approaches and can be applied to other materials such as perovskite and thin film.