Numerical approximation to Benjamin type equations. Generation and stability of solitary waves

© 2018 Elsevier B.V. This paper is concerned with the study, by computational means, of the generation and stability of solitary-wave solutions of generalized versions of the Benjamin equation. The numerical generation of the solitary-wave profiles is accurately performed with a modified Petviashvili method which includes extrapolation to accelerate the convergence. In order to study the dynamics of the solitary waves the equations are discretized in space with a Fourier pseudospectral collocation method and a fourth-order, diagonally implicit Runge–Kutta method of composition type as time-stepping integrator. The stability of the waves is numerically studied by performing experiments with small and large perturbations of the solitary pulses as well as interactions of solitary waves

Can machine learning reduce the number of anode readouts for reconstruction of coincident single photons in CDIR resistive sea photon detectors?

This study focuses on exploring the potential of Charge Division Imaging Readout (CDIR) for micro-Channel plate (MCP) based resistive sea photon detectors. The CDIR technique spreads the MCP charge footprint capacitively between readout nodes forming anode segments. Charge measurements at each node are then used to reconstruct incident photon's position and time. A primary objective is to investigate the minimum number of anode segmentation's necessary, to allow successful reconstruction of multiple photons within a given time interval where pile up would be an issue for traditional approaches. Allowing for optimisation of the anode structure, investigating for a readout schematic to improve timing, rate capability, and reduce distortion effects. Algorithmic and machine learning (ML) techniques will be compared and utilised to reconstruct spatial positions of multiple photons. The comparison will aim to determine if machine learning techniques can be utilised to correct for algorithmic systematic errors to provide a more robust system, whilst removing the need for complex calibrations and allowing for efficient implementation on FPGA in future work.</p

Investigating machine learning solutions for a 256 channel TCSPC camera with sub-70 ps single photon timing per channel at data rates >10 Gbps

The development of a Time Correlated Single Photon Counting (TCSPC) camera with 256 channels has enabled several applications where single photon sensitivity is crucial, such as LiDAR, Fluorescent Lifetime IMaging (FLIM) and quantum information systems. The microchannel plate-based Multi-Anode Photo-Multiplier Tube (MAPMT) is a 16 × 16 array of 1.656 mm pitch pixels with an active anode area of 26.5 × 26.5 mm2. Each pixel can time single photons with an accuracy of 60 ps rms at a maximum photon rate of 480 KHz. The timing electronics are capable of measuring 120 Mcps, producing huge volumes of data for processing, in the region of 10 Gbps per detector. Limitations in algorithmic data analysis techniques are critical for this application, hence this paper demonstrates a machine learning (ML) model which can determine the photon event coordinates with the objective of processing each one photon per 10 μs. The model applies calibrations for the detector and electronics such as amplitude walk, and charge measurement and channel to channel threshold variation. Optimisation of the model is detailed within the paper, including training hyperparameters, the clustering of coincident events and compression of the model through pruning techniques. The ML model is trained and tested on a simulation of the microchannel plate (MCP) PMT with timing electronics configured for use as a TCSPC camera, utilising charge sharing techniques to further improve the spatial resolution of the detector. Further objectives of the research are to test the model on detector data, allowing assessment on the variance of accuracy between simulated and real data. Beyond this, assessment of the performance of this approach compared to algorithmic approaches and introduction of statistical reasoning of the robustness of the model will be completed