A GPU-Accelerated Transient Detection Pipeline for DECam Time-Domain Surveys

TL;DR

This paper introduces a GPU-accelerated transient detection pipeline for DECam time-domain surveys, enabling real-time processing, high accuracy in transient identification, and efficient candidate filtering for rapid astronomical discoveries.

Contribution

The paper presents a novel GPU-based pipeline that significantly accelerates transient detection and incorporates advanced filtering and classification methods for improved survey efficiency.

Findings

Achieves ~99% completeness in real transient detection

Rejects ~96% of artifacts with CNN classifier

Processes each DECam exposure in ~50 seconds

Abstract

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 $\sim$ 99\% of real transients while rejecting $\sim$ 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 $\sim$ 50 s from calibrated image processing to alert generation using a modest allocation of computing resources.