Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.Comment: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Societ

The Future Landscape of High-Redshift Galaxy Cluster Science

Large scale structure and cosmolog

LoVoCCS. I. Survey Introduction, Data Processing Pipeline, and Early Science Results

We present the Local Volume Complete Cluster Survey (LoVoCCS; we pronounce it as low-vox or law-vox, with stress on the second syllable), an NSF\u27s National Optical-Infrared Astronomy Research Laboratory survey program that uses the Dark Energy Camera to map the dark matter distribution and galaxy population in 107 nearby (0.03 \u3c z \u3c 0.12) X-ray luminous ([0.1-2.4 keV] L X500 \u3e 1044 erg s-1) galaxy clusters that are not obscured by the Milky Way. The survey will reach Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Year 1-2 depth (for galaxies r = 24.5, i = 24.0, signal-to-noise ratio (S/N) \u3e 20; u = 24.7, g = 25.3, z = 23.8, S/N \u3e 10) and conclude in 1/42023 (coincident with the beginning of LSST science operations), and will serve as a zeroth-year template for LSST transient studies. We process the data using the LSST Science Pipelines that include state-of-the-art algorithms and analyze the results using our own pipelines, and therefore the catalogs and analysis tools will be compatible with the LSST. We demonstrate the use and performance of our pipeline using three X-ray luminous and observation-time complete LoVoCCS clusters: A3911, A3921, and A85. A3911 and A3921 have not been well studied previously by weak lensing, and we obtain similar lensing analysis results for A85 to previous studies. (We mainly use A3911 to show our pipeline and give more examples in the Appendix.