A tunable metamaterial absorber (MMA) by reversible phase transitions in a mid-infrared regime is theoretically investigated. The absorber is composed of a molybdenum (Mo)-germanium-antimony-tellurium (Ge2Sb2Te5, GST)-Mo nanodisk structure superimposed on the GST-Al2O3 (aluminum oxide)-Mo film. Studies have shown that the combination of the inlaid metal-medium dielectric waveguide mode and the resonant cavity mode and the excitation of the propagating surface plasmon mode are the main reasons for the formation of the triple-band high absorption. Additionally, through the reversible phase change, the transition from high absorption to high reflection in the mid-infrared region is realized. The symmetry of the absorber eliminates the polarization dependence, and the near unity absorption efficiency can be maintained by incidence angles up to 60°. The presented method will enhance the functionality of the absorber and has the potential for the applications that require active control over light absorption.
Retrieval of particle size distribution from bulk optical properties based on evolutionary algorithms is usually computationally expensive. In this paper, we report an efficient numerical approach to solving the inverse scattering problem by accelerating the calculation of bulk optical properties based on machine learning. With the assumption of spherical particles, the forward scattering by particles is first solved by Mie scattering theory and then approximated by machine learning. The particle swarm optimization algorithm is finally employed to optimize the particle size distribution parameters by minimizing the deviation between the target and simulated bulk optical properties. The accuracies of machine learning and particle swarm optimization are separately investigated. Meanwhile, both monomodal and bimodal size distributions are tested, considering the influences of random noise. Results show that machine learning is capable of accurately predicting the scattering efficiency for a specific size distribution in approximately 0.5 µs on a standalone computer. Therefore, the proposed method has the potential to serve as a powerful tool in real-time particle size measurement due to its advantages of simplicity and high efficiency.
We investigate the measurement of the sixth order cumulant and its ratio to the second order cumulant (C 6 /C 2 ) in relativistic heavy-ion collisions. The influence of statistics and different methods of centrality bin width correction on C 6 /C 2 of net-proton multiplicity distributions is demonstrated. There is no satisfactory method to extract C 6 /C 2 with the current statistics recorded at lower energies by STAR at RHIC. With statistics comparable to the expected statistics at the planned future RHIC Beam Energy Scan II (BES II), no energy dependence of C 6 /C 2 is observed in central collisions using the UrQMD model. We find if the transition signal is as strong as predicted by the PQM model, then it is hopefully observed at the upcoming RHIC BES II.
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