We present a GPU-accelerated transient detection pipeline developed for time-domain surveys with the Dark Energy Camera (DECam). It enables real-time-capable image processing, incorporating science-driven candidate filtering to support rapid transient identification in time-critical observing programs. The pipeline serves as the core transient discovery engine for multiple long-term DECam programs, including the GW-MMADS gravitational-wave follow-up campaign and the DESIRT survey for intermediate-redshift transients with DESI synergy. The pipeline ingests calibrated imaging products from the DECam Community Pipeline and performs image differencing using the SFFT algorithm, coupled with CNN-based real-bogus classification, to produce science-ready transient alerts and light curves that are delivered to community brokers. We validate the pipeline using archival DECam data from the DESIRT survey. The real-bogus classifier achieves a completeness of ∼ 99\% of real transients while rejecting ∼ 96\% of subtraction artifacts, and the workflow typically reduces the candidate load to a manageable level for survey operations. With GPU acceleration, the typical processing time per DECam exposure is ∼ 50 s from calibrated image processing to alert generation using a modest allocation of computing resources.
@article{arxiv.2603.08593,
title = {A GPU-Accelerated Transient Detection Pipeline for DECam Time-Domain Surveys},
author = {Lei Hu and Tomás Cabrera and Antonella Palmese and Lifan Wang and Igor Andreoni and Xander J. Hall and Xingzhuo Chen and Jiawen Yang and Frank Valdes and Brendan O'Connor and Yuhan Chen},
journal= {arXiv preprint arXiv:2603.08593},
year = {2026}
}