Astronomical Pointlike Source Detection via Deep Feature Matching

Abstract This study introduces PSDetNet, an innovative deep neural network tailored for the autonomous detection of pointlike astronomical sources by leveraging feature-matching techniques. PSDetNet comprises two primary modules: feature extraction and matching localization. The feature extraction module is built on residual blocks and adopts an encoder–decoder framework to distill features from images robustly. The matching localization module employs a patch-by-patch comparison against a preconstructed template, which is crafted through the alignment and weighted aggregation of numerous exemplar pointlike source samples, capturing the quintessential distribution characteristics of pointlike sources. The experimental results demonstrate that this network can accurately detect pointlike sources in astronomical imagery with high purity and completeness. It operates end to end and uses a fully convolutional architecture that allows for flexible processing of images of any size. This ability considerably enhances its applicability across various practical scenarios.