PUMA: The Positional Update and Matching Algorithm

TL;DR

PUMA is a new software tool that improves cross-matching of low-frequency radio sources by combining Bayesian positional matching with spectral criteria, enabling accurate sky modeling and foreground removal.

Contribution

The paper introduces PUMA, a novel algorithm that integrates positional Bayesian methods with spectral matching for radio catalog cross-matching, and demonstrates its effectiveness with real and simulated data.

Findings

Successfully cross-matched 98.5% of sources in the sky model.

Accurately recovered spectral indices and ionospheric offsets.

Enhanced foreground removal using higher frequency and resolution data.

Abstract

We present new software to cross-match low-frequency radio catalogues: the Positional Update and Matching Algorithm (PUMA). PUMA combines a positional Bayesian probabilistic approach with spectral matching criteria, allowing for confusing sources in the matching process. We go on to create a radio sky model using PUMA based on the Murchison Widefield Array Commissioning Survey, and are able to automatically cross-match 98.5% of sources. Using the characteristics of this sky model, we create simple simulated mock catalogues on which to test PUMA, and find that PUMA can reliably find the correct spectral indices of sources, along with being able to recover ionospheric offsets. Finally, we use this sky model to calibrate and remove foreground sources from simulated interferometric data, generated using OSKAR (the Oxford University visibility generator). We demonstrate that there is a substantial improvement in foreground source removal when using higher frequency and higher resolution source positions, even when correcting positions by an average of 0.3 given a synthesized beam-width of 2.3.