Speaker
Description
We present a GPU-native simulation framework for liquid-argon neutrino detectors that enables efficient simulation and signal processing of sparse data in dense GPU-oriented workflows. Traditional dense FFT-based methods in liquid-argon detector simulation are adapted to sparse signals through blockwise processing and analytic tensor operations. We develop an efficient block-sparse binned tensor abstraction that preserves detector locality, supports batched parallel execution on GPUs, and reduces memory usage. We also introduce a new algorithm for signal processing on zero-suppressed data, overcoming the traditional assumption of dense full-waveform input. Implemented in PyTorch, the framework bridges machine-learning software ecosystems and high-performance scientific computing for next-generation neutrino experiments, using the liquid-argon time projection chamber of the DUNE Near Detector as a representative case study.
