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The American Astronomical Society (AAS) Journals are a vital asset of our professional society. With the push towards open access, page charges are a viable and sustainable option for continuing to effectively fund and publish the AAS Journals. However, the existing page charge model, which requires individual authors to pay page charges out of their grants or even out of pocket, is already challenging to some researchers and could be exacerbated in the Open Access (OA) era if charges increase. A discussion of alternative models for funding page charges and publishing costs should be part of the Astro2020 decadal survey if we wish to continue supporting the sustainable and accessible publication of US research in AAS journals in the rapidly-shifting publication landscape. The AAS Publications Committee recommends that the National Academy of Sciences form a task force to develop solutions and recommendations with respect to the urgent concerns and considerations highlighted in this White Paper.
Cool, evolved stars are the main source of chemical enrichment of the interstellar medium (ISM), and understanding their mass loss and structure offers a unique opportunity to study the cycle of matter in the Universe. Pulsation, convection, and othe
Wide-angle surveys have been an engine for new discoveries throughout the modern history of astronomy, and have been among the most highly cited and scientifically productive observing facilities in recent years. This trend is likely to continue over
(Abridged) The Truth and Reconciliation Commission of Canada published its calls to action in 2015 with 94 recommendations. Many of these 94 recommendations are directly related to education, language, and culture, some of which the Canadian Astronom
Far-infrared astronomy has advanced rapidly since its inception in the late 1950s, driven by a maturing technology base and an expanding community of researchers. This advancement has shown that observations at far-infrared wavelengths are important
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML met