Bayesian Inference for Radio Observations - Going beyond deconvolution

TL;DR

This paper introduces BIRO, a Bayesian inference method that directly analyzes raw radio interferometry data to estimate scientific parameters and instrumental errors, overcoming limitations of traditional deconvolution algorithms.

Contribution

The paper presents a novel Bayesian framework for radio data analysis that jointly estimates scientific parameters and instrumental errors directly from raw data.

Findings

Successfully demonstrated BIRO on simulated Westerbork data

Able to estimate uncertainties and instrumental effects simultaneously

Outperforms traditional deconvolution in error estimation

Abstract

Radio interferometers suffer from the problem of missing information in their data, due to the gaps between the antennas. This results in artifacts, such as bright rings around sources, in the images obtained. Multiple deconvolution algorithms have been proposed to solve this problem and produce cleaner radio images. However, these algorithms are unable to correctly estimate uncertainties in derived scientific parameters or to always include the effects of instrumental errors. We propose an alternative technique called Bayesian Inference for Radio Observations (BIRO) which uses a Bayesian statistical framework to determine the scientific parameters and instrumental errors simultaneously directly from the raw data, without making an image. We use a simple simulation of Westerbork Synthesis Radio Telescope data including pointing errors and beam parameters as instrumental effects, to demonstrate the use of BIRO.