GPUPhot: Kernel-Based Algorithms for Point-Source Detection and Photometry with a Spatially Variable PSF

Abstract

Modern imaging surveys generate data at rates that exceed the throughput of photometry pipelines built on iterative, per-source fitting, a construct that does not map onto parallel hardware. We present a set of algorithms for point-source detection and photometry reformulated so that the entire reduction runs natively on a Graphics Processing Unit (GPU), implemented in GPUPhot. Their design rests on a single principle: every stage is cast as a convolution with a fixed kernel or an element-wise operation on full-frame maps. The algorithms are designed to operate on wide-field images with a spatially variable Point Spread Function (PSF), where the field-dependent PSF is described through a small set of fixed, flux-conserving principal-component eigenmodes with position-dependent coefficients, so that the spatially varying matched filter decomposes exactly into a few genuine convolutions while retaining its maximum-likelihood foundation. We show that, even in fields with strong background gradients and markedly inhomogeneous PSFs, the framework achieves sub-pixel astrometric accuracy and few-millimagnitude photometric precision for bright sources. The pipeline is already in production at the telescopes of the Teide Observatory, where it has processed 3.7 million images since 2023, amounting to 1.2 PB of data, at effective end-to-end rates below 15 s per 150-Mpx image, from ingestion of the raw frame to a source catalog with astrometric and photometric solutions. Because the same primitives underlie every stage, the individual algorithms are self-contained and applicable well beyond photometry. They form the basis of a complete, openly developed software package, released publicly with this paper.