Yuhan Chen

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 SFFT, a GPU-accelerated Fourier-domain image-subtraction method, coupled with convolutional neural network-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. Our AI-based 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.