Detecting Unspecified Structure in Low-Count Images

TL;DR

This paper introduces a Bayesian method to detect significant structures in low-count astronomical images, distinguishing true features from noise with limited computational resources.

Contribution

It presents a novel Bayesian testing approach using a multiscale model and tail probability as a test statistic for image structure detection.

Findings

Method accurately detects true structures in simulated images.

Applied to real X-ray data, it identified an X-ray jet.

Provides computationally efficient p-value bounds.

Abstract

Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet.