New approaches for faint source detection in hard X-ray surveys

TL;DR

This paper introduces two innovative methods for faint source detection in hard X-ray surveys, utilizing a neural network and Bayesian reasoning to improve sensitivity, reduce human intervention, and identify previously undetected sources.

Contribution

The paper presents a convolutional neural network for more sensitive, faster, and less subjective source detection, and a Bayesian method for better combining multiple observations, advancing hard X-ray survey analysis.

Findings

Neural network outperforms previous detection methods in sensitivity.

Bayesian approach improves detection consistency across observations.

Identified 25 new sources in INTEGRAL data, about 5% of total sources.

Abstract

We demonstrate two new approaches that have been developed to aid the production of future hard X-ray catalogs, and specifically to reduce the reliance on human intervention during the detection of faint excesses in maps that also contain systematic noise. A convolutional neural network has been trained on data from the INTEGRAL/ISGRI telescope to create a source detection tool that is more sensitive than previous methods, whilst taking less time to apply to the data and reducing the human subjectivity involved in the process. This new tool also enables searches on smaller observation timescales than was previously possible. We show that a method based on Bayesian reasoning is better able to combine the detections from multiple observations than previous methods. When applied to data from the first 1000 INTEGRAL revolutions these improved techniques detect 25 sources (about 5% of the total sources) which were previously undetected in the stacked images used to derive the published catalog made using the same dataset.