Study of photon detection efficiency and position resolution of BESIII electromagnetic calorimeter

We study the photon detection efficiency and position resolution of the electromagnetic calorimeter (EMC) of the BESIII experiment. The control sample of the initial-state-radiation (ISR) process of $e^+e^-\rightarrow \gamma \mu^+\mu^-$ is used at $J/\psi$ and $\psi(3770)$ resonances for the EMC calibration and photon detection efficiency study. Photon detection efficiency is defined as the predicted photon, obtained by performing a kinematic fit with two muon tracks, matched with real photons in the EMC. The spatial resolution of the EMC is defined as the separation in polar ($\theta$) and azimuthal ($\phi$) angles between charged track and associated cluster centroid on the front face of the EMC crystals.Comment: 5 page

Collisional-Radiative Model for the visible spectrum of W26+W^{26+} ions

Plasma diagnostics in magnetic confinement fusion plasmas by using visible spectrum strongly depends on the knowledge of fundamental atomic properties. A detailed collisional-radiative model of W$^{26+}$ ions has been constructed by considering radiative and electron excitation processes, in which the necessary atomic data had been calculated by relativistic configuration interaction method with the implementation of Flexible Atomic Code. The visible spectrum observed at an electron beam ion trap (EBIT) in Shanghai in the range of 332 nm to 392 nm was reproduced by present calculations. Some transition pairs of which the intensity ratio are sensitive to the electron density were selected as potential candidate of plasma diagnostics. Their electron density dependence are theoretically evaluated for the cases of EBIT plasmas and magnetic confinement fusion plasmas

Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm