With the start of a new Great Observatories era, there is renewed concern that the demand for these forefront facilities, through proposal pressure, will exceed conventional peer-review management's capacity for ensuring an unbiased and efficient selection. There is need for new methods, strategies, and tools to facilitate those reviews. Here, we describe PACMan2, an updated tool for proposal review management that utilizes machine learning models and techniques to topically categorize proposals and reviewers, to match proposals to reviewers, and to facilitate proposal assignments, mitigating some conflicts of interest. We find that the classifier has cross-validation accuracy of 80.0±2.2% on proposals for time on the Hubble Space Telescope and the James Webb Space Telescope.
@article{arxiv.2303.04220,
title = {PACMan2: Next Steps in Proposal Review Management},
author = {Louis-Gregory Strolger and Jamila Pegues and Tegan King and Nathan Miles and Michelle Ramsahoye and Keith Ceruti and Brett Blacker and I. Neill Reid},
journal= {arXiv preprint arXiv:2303.04220},
year = {2023}
}