The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

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Plasmonic sensors provide label-free detection of bio and chemical targets with ultrahigh sensitivity and accuracy. However, they usually lack the ability to operate at high temperatures, producing large measurement errors due to perturbations. Here, we report a surface plasmon resonance sensor based on Al–Au thin films, which outperforms its conventional Au counterpart by providing a temperature-stable response. We fabricate six metallic samples via the co-sputtering depositing method, obtaining four bimetallic thin films and two pure metals. Through spectroscopic ellipsometry, transmission measurements, and scanning electron microscopy images, we obtain their dielectric function and film morphology from room temperature to 200 °C, showing that the films containing Al do not undergo significant changes with increasing temperature. We experimentally and theoretically establish the dispersion relation of Al–Au alloys by varying the film chemical composition. Using the transfer matrix method, we evaluate the performance of the sensors by studying their response in the refractive index measurement of air, water, and a biological environment. We show that all alloys outperform their pure counterparts, achieving maximum theoretical sensitivities of 42411 nm/RIU and 162.7 °/RIU for a Au0.62Al0.38-based wavelength-dependent sensor and a Au0.85Al0.15-based angular-dependent sensor, respectively. We find that Au0.85Al0.15 is a particularly promising candidate for both wavelength- and angular-dependent sensors due to its high sensitivity (18967 nm/RIU and 162.7 °/RIU) and good peak definition. Furthermore, using partial density-of-state calculations assisted by machine learning, we obtain the dielectric function of the films, showing an excellent agreement with our experimental results. The alloying approach assisted by computational prediction of the samples’ physical properties has the potential to accelerate the discovery of novel materials for plasmonic sensors with high sensitivity and excellent functioning capabilities at elevated temperatures.