Assistant Professor of Physics Jillian Scudder is a coauthor on two new publications

Teimoorinia, H., M. Jalilkhany, Jillian M. Scudder, J. Jensen, and S. L. Ellison. 2021. A reassessment of strong line metallicity conversions in the machine learning era. Monthly Notices of the Royal Astronomical Society 503:1082-1095. https://doi.org/10.1093/mnras/stab466

From the Abstract: 

The random forest (RF) algorithm [developed by the authors] is non-parametric and therefore more flexible than polynomial conversions, due to its ability to capture non-linear behaviour in the data. The RF method yields the same accuracy as the (updated) polynomial conversions, but has the significant advantage that a single model can be applied over a wide range of metallicities, without the need to distinguish upper and lower branches in R-23 calibrations. The trained RF is made publicly available for use in the community. Access on ArXiv (preprint version, Feb. 16, 2021)

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Also accessible on ArXiv is this publication describing a project that allows easier study of distant galaxies across wavelength.

Shirley, R., K. Duncan ...J. Scudder, et al. 2021. HELP: The Herschel Extragalactic Legacy Project. arXiv:2105.05659 [astro-ph.GA]

From the Abstract: 
With this project definition paper we provide full access to the first data release of HELP; Data Release 1 (DR1), including a monolithic map of the largest SPIRE extragalactic field at 385 deg2 and 18 million measurements of PACS and SPIRE fluxes. We also provide tools to access and analyse the full HELP database. This new data set includes far-infrared photometry, photometric redshifts, and derived physical properties estimated from modelling the spectral energy distributions.