Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
This work addresses the challenge of accurate cataloging in ground-based astronomy by accounting for spatial variations, though it is incremental as it extends an existing method to a new scenario.
The authors tackled the problem of constructing probabilistic catalogs of light sources from astronomical images with spatially varying backgrounds and point spread functions (PSFs), which had not been addressed by neural posterior estimation (NPE) before, and demonstrated its effectiveness on Sloan Digital Sky Survey data for detection, separation, and flux measurement.
Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images have spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a method of performing NPE with spatially varying backgrounds and PSFs. In this method, we generate synthetic catalogs and semi-synthetic images for these catalogs using randomly sampled PSF and background estimates from existing surveys. Using this data, we train a neural network, which takes an astronomical image and representations of its background and PSF as input, to output a probabilistic catalog. Our experiments with Sloan Digital Sky Survey data demonstrate the effectiveness of NPE in the presence of spatially varying backgrounds and PSFs for light source detection, star/galaxy separation, and flux measurement.