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The proposed next-generation Very Large Array (ngVLA) will enable the imagingof astronomical sources in unprecedented detail by providing an order ofmagnitude improvement in sensitivity and angular resolution compared with radiointerferometers currently operating at 1.2--116~GHz. However, the current ngVLAarray design results in a highly non-Gaussian dirty beam that may make itdifficult to achieve high-fidelity images with both maximum sensitivity andmaximum angular resolution using traditional CLEAN deconvolution methods. Thischallenge may be overcome with regularized maximum-likelihood (RML) methods, anew class of imaging techniques developed for the Event Horizon Telescope. RMLmethods take a forward-modeling approach, directly solving for the imageswithout using either the dirty beam or the dirty map. Consequently, this methodhas the potential to improve the fidelity and effective angular resolution ofimages produced by the ngVLA. As an illustrative case, we present ngVLA imagingsimulations of stellar radio photospheres performed with both multi-scale (MS-)CLEAN and RML methods implemented in the CASA and SMILI packages, respectively.We find that both MS-CLEAN and RML methods can provide high-fidelity imagesrecovering most of the representative structures for different types of stellarphotosphere models. However, RML methods show better performance than MS-CLEANfor various stellar models in terms of goodness-of-fit to the data, residualerrors of the images, and in recovering representative features in the groundtruth images. Our simulations support the feasibility of transformative stellarimaging science with the ngVLA, and simultaneously demonstrate that RML methodsare an attractive choice for ngVLA imaging.

Exploring Regularized Maximum Likelihood Reconstruction for Stellar Imaging with the ngVLA