Improved constraints on non-Newtonian forces at 10 microns

Several recent theories suggest that light moduli or particles in "large" extra dimensions could mediate macroscopic forces exceeding gravitational strength at length scales below a millimeter. Such new forces can be parameterized as a Yukawa-type correction to the Newtonian potential of strength $\alpha$ relative to gravity and range $\lambda$. To extend the search for such new physics we have improved our apparatus utilizing cryogenic micro-cantilevers capable of measuring attonewton forces, which now includes a switchable magnetic force for calibration. Our most recent experimental constraints on Yukawa-type deviations from Newtonian gravity are more than three times as stringent as our previously published results, and represent the best bound in the range of 5 - 15 microns, with a 95 percent confidence exclusion of forces with $|\alpha| > 14,000$ at $\lambda$ = 10 microns.Comment: 12 pages, 9 figures, accepted for publication in PRD. Minor changes, replaced and corrected Figs 4,5,

Detangling the role of climate in vegetation productivity with an explainable convolutional neural network

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understand future productivity dynamics; our approach leverages the capacities of the V-Net architecture for spatiotemporal LAI prediction. Preliminary results are well-aligned with established quality standards of LAI products estimated from Earth observation data. We hope that this work serves as a robust foundation for subsequent research endeavours, particularly for the incorporation of prediction attribution methodologies, which hold promise for elucidating the underlying climate change drivers of global vegetation productivity.Comment: 7 pages, 2 figures, submitted to Tackling Climate Change with Machine Learning at NeurIPS 202