Solving Self-calibration of ALMA Data with an Optimization Method

TL;DR

This paper introduces an optimization-based method for self-calibration of ALMA radio interferometry data, integrating gain correction and imaging into a single regularized optimization framework, leading to promising results.

Contribution

It reformulates the self-calibration process as a unified optimization problem, combining gain correction and imaging for improved accuracy.

Findings

Effective gain correction demonstrated on ALMA data

Unified optimization approach improves calibration accuracy

Promising results compared to traditional methods

Abstract

We reformulate the gain correction problem of the radio interferometry as an optimization problem with regularization, which is solved efficiently with an iterative algorithm. Combining this new method with our previously proposed imaging method, PRIISM, the whole process of the self-calibration of radio interferometry is redefined as a single optimization problem with regularization. As a result, the gains are corrected, and an image is estimated. We tested the new approach with ALMA observation data and found it provides promising results.