Year: 2026
Type: article
Abstract: Mapping the boundary of an extended source is a key step in the study of its morphology. The background contamination and statistical fluctuations of typical astronomical images make this a challenging statistical task, in particular for X-ray images with low surface brightness. We develop a three-step Bayesian procedure to identify the boundaries of irregularly shaped sources. We first apply a Bayesian multiscale reconstruction algorithm known as LIRA to obtain posterior pixel-wise probability distributions of the source intensity that properly account for known structures, astrophysical background, and the effect of the telescope point-spread function. Next, we adopt an Ising model to group pixels with similar intensities into cohesive regions corresponding to background and source. Finally, the boundary is derived on the basis of the most likely aggregation of pixels into the source region. Because the overall model combines LIRA and the Ising model, we call it LIRA–Ising. We verify the proposed method using a set of simulation studies. We then apply it to the Chandra X-ray Observatory images of two high-redshift quasars, PKS J1421−0643 and 0730+257, to determine the extent and morphology of X-ray jets. Our method shows a uniform X-ray surface brightness of PKS J1421−0643’s jet, and it identifies knotty structure in the X-ray jet of 0730+257.
Authors: Kathryn McKeough, Vinay L. Kashyap, Aneta Siemiginowska, David A. van Dyk, Shihao Yang, Xiao-Li Meng, Brendan Martin, Andreas Zezas
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
Awards: G1390571956, Development of Novel Statistical Tools for the Analysis of Astronomical Data, G3345895242, G3477743816, G4619009207, G5780980149, DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers from and for Emerging Astronomical Data Challenges, G8367957132, EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning, G8821442135
OpenAlex ID: W7130320042
