GPU-Accelerated Analytic Simulation of Sparse Signals in Pixelated Time Projection Detector

TL;DR

This paper introduces TRED, a GPU-accelerated simulation tool for pixelated neutrino detectors, featuring innovative charge calculation and sparse data representation methods that improve efficiency and scalability.

Contribution

The paper presents novel GPU-based algorithms for effective-charge calculation and sparse tensor representation, enhancing simulation efficiency for large-scale neutrino detectors.

Findings

Efficient GPU implementation of charge calculation using Gaussian quadrature.

Sparse tensor representation enables scalable FFT-based computations.

Benchmark results demonstrate low memory usage and fast runtimes.

Abstract

This paper presents a GPU-accelerated simulation package, TRED, for next-generation neutrino detectors with pixelated charge readout, leveraging community-driven software ecosystems to ensure sustainability and extensibility. We introduce two generic contributions: (i) an effective-charge calculation based on Gaussian quadrature rules for numerical integration, and (ii) a sparse, block-binned tensor representation that enables efficient FFT-based computation of induced signals on readout electrodes for sparsely activated detector volumes. The former captures sub-grid structure without requiring dense sampling, while the latter achieves low memory usage and scalable runtime, as demonstrated in benchmark studies. The underlying data representation is applicable to large-scale detectors and to other computational problems involving sparse activity.