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Relative fractions and phases of the intermediate decays are determined. With the detection efficiency estimated by the results of the amplitude analysis, the branching fraction of Dþ s → K−Kþπþπ0 decay is measured to be ð5.42 0.10stat 0.17systÞ%.
Using 448.1 × 106 ψ(3686) decays collected with the BESIII detector at the BEPCII e+e− storage rings, the branching fractions and angular distributions of the decays χcJ → Ξ−Ξ¯¯¯¯+ and Ξ0Ξ¯¯¯¯0 (J = 0, 1, 2) are measured based on a partial-reconstruction technique. The decays χc1 → Ξ0Ξ¯¯¯¯0 and χc2 → Ξ0Ξ¯¯¯¯0 are observed for the first time with statistical significances of 7σ and 15σ, respectively. The results of this analysis are in good agreement with previous measurements and have significantly improved precision.
We report a measurement of the observed cross sections of e+ e− → J/ψX based on 3.21 fb − 1 of data accumulated at energies from 3.645 to 3.891 GeV with the BESIII detector operated at the BEPCII collider. In analysis of the cross sections, we measured the decay branching fractions of B(ψ(3686) → J/ψX) = (64.4 ± 0.6 ± 1.6)% and B(ψ(3770) → J/ψX) = (0.5 ± 0.2 ± 0.1)% for the first time. The energy-dependent line shape of these cross sections cannot be well described by two Breit-Wigner (BW) amplitudes of the expected decays ψ (3686) → J/ψX and ψ(3770) → J/ψX. Instead, it can be better described with one more BW amplitude of the decay R(3760)→ J/ψX. Under this assumption, we extracted the R (3760) mass M R (3760 ) = 3766.2 ± 3.8 ± 0.4 MeV/c2, total width Γ tot R ( 3760 ) = 22.2 ± 5.9 ± 1.4 MeV, and product of leptonic width and decay branching fraction
ΓeeR(3760) B[R(3760) → J/ψX] = (79.4 ± 85.5 ± 11.7) eV. The significance of the R(3760) is 5.3σ. The first uncertainties of these measured quantities are from fits to the cross sections and second systematic.
Using 10.1 × 109 J/ψ events produced by the Beijing Electron Positron Collider (BEPCII) at a center-of-mass energy √s = 3.097 GeV and collected with the BESIII detector, we present a search for the rare semi-leptonic decay J/ψ → D−e+νe + c.c. No excess of signal above background is observed, and an upper limit on the branching fraction ℬ(J/ψ → D−e+νe + c. c.) < 7.1 × 10−8 is obtained at 90% confidence level. This is an improvement of more than two orders of magnitude over the previous best limit.
The electromagnetic process is studied with the initial-state-radiation technique using 7.5 fb−1 of data collected by the BESIII experiment at seven energy points from 3.773 to 4.600 GeV. The Born cross section and the effective form factor of the proton are measured from the production threshold to 3.0 GeV/ using the invariant-mass spectrum. The ratio of electric and magnetic form factors of the proton is determined from the analysis of the proton-helicity angular distribution.
Using a data sample of e+e− collision data corresponding to an integrated luminosity of 2.93 fb−1 collected with the BESIII detector at a center-of-mass energy of s=3.773GeV, we search for the singly Cabibbo-suppressed decays D0→π0π0π0, π0π0η, π0ηη and ηηη using the double tag method. The absolute branching fractions are measured to be B(D0→π0π0π0)=(2.0±0.4±0.3)×10−4, B(D0→π0π0η)=(3.8±1.1±0.7)×10−4 and B(D0→π0ηη)=(7.3±1.6±1.5)×10−4 with the statistical significances of 4.8σ, 3.8σ and 5.5σ, respectively, where the first uncertainties are statistical and the second ones systematic. No significant signal of D0→ηηη is found, and the upper limit on its decay branching fraction is set to be B(D0→ηηη)<1.3×10−4 at the 90% confidence level.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.