W. H. Ng | Searcharxiv

arXiv2025

Photomultiplier Requirements and Pre-Calibration for the SABRE South Liquid Scintillator Veto

We present a study of the oil-proof base Hamamatsu R5912 photomultiplier tubes that will be used in the SABRE South linear-alkylbenzene liquid scintillator veto. SABRE South is a dark matter direct detection experiment at the Stawell Underground Physics Laboratory, aiming to test the DAMA/LIBRA dark matter annual modulation signal. We discuss the requirements of the liquid scintillator system and its photomultipliers, outline the methods and analysis used for the characterisation measurements, and results from initial tests. We discuss the impact of these measurements on the performance of the active veto system and explore analysis methods to allow for low threshold operation. Finally, we include results from a small scale liquid scintillator detector prototype used to assess the future performance of pulse shape discrimination in the liquid scintillator veto, and how well accommodated it is by the R5912 PMTs.

physics.ins-det↗

arXiv2020

Committee machines -- a universal method to deal with non-idealities in memristor-based neural networks

Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science -- committee machines -- in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.

cs.ET↗