Measurement of proton electromagnetic form factors in the time-like region using initial state radiation at BESIII
Physics Letters B Elsevier 817 (2021) 136328
Measurement of the absolute branching fraction of Λ c + → p K S 0 η decays
Physics Letters B Elsevier 817 (2021) 136327
Measurement of the D -> K-pi(+)pi(+)pi(-) and D -> K-pi(+)pi(0) coherence factors and average strong-phase differences in quantum-correlated D(D)over-bar decays
JOURNAL OF HIGH ENERGY PHYSICS Springer Science and Business Media LLC 2021:5 (2021) ARTN 164
Abstract:
The decays D → K π π π and D → K π π are studied in a sample of quantum-correlated DD¯ pairs produced through the process e e → ψ(3770) → DD¯ , exploiting a data set collected by the BESIII experiment that corresponds to an integrated luminosity of 2.93 fb . Here D indicates a quantum superposition of a D and a D¯ meson. By reconstructing one neutral charm meson in a signal decay, and the other in the same or a different final state, observables are measured that contain information on the coherence factors and average strong-phase differences of each of the signal modes. These parameters are critical inputs in the measurement of the angle γ of the Unitarity Triangle in B → DK decays at the LHCb and Belle II experiments. The coherence factors are determined to be R = 0.52−0.10+0.12 and RKππ0 = 0.78 ± 0.04, with values for the average strong-phase differences that are δDK3π=(167−19+31)° and δDKππ0=(196−15+14)°, where the uncertainties include both statistical and systematic contributions. The analysis is re-performed in four bins of the phase-space of the D → K π π π to yield results that will allow for a more sensitive measurement of γ with this mode, to which the BESIII inputs will contribute an uncertainty of around 6°. [Figure not available: see fulltext.] − + + − − + 0 + − −1 0 − − − + + − K3πSupernova neutrino burst detection with the Deep Underground Neutrino Experiment
The European Physical Journal C SpringerOpen 81:5 (2021) 423
Abstract:
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programsSearch for a heavy Higgs boson decaying into a Z boson and another heavy Higgs boson in the $$\ell \ell bb$$ and $$\ell \ell WW$$ final states in pp collisions at $$\sqrt{s}=13$$ $$\text {TeV}$$ with the ATLAS detector
The European Physical Journal C SpringerOpen 81:5 (2021) 396