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Quantum many-body scars are notable as nonthermal, low-entanglement states that exist at high energies. In this study, we used attractively interacting dysprosium gases to create scar states that are stable enough to be driven into a strongly nonlinear regime while retaining their character. We measured how the kinetic and total energies evolve after quenching the confining potential. Although the bare interactions are attractive, the atoms behave as if they repel each other: Their kinetic energy paradoxically decreases as the gas is compressed. The missing “phantom” energy is quantified by benchmarking our experimental results against generalized hydrodynamics calculations. We present evidence that the missing kinetic energy is carried by undetected, very high momentum atoms. 

The accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environmental management. While recent studies have leveraged the spatial characteristics of hydrologic features, the use of elevation models for identifying drainage paths can be influenced by flow barriers. To address these challenges, our focus in this study is on detecting drainage crossings through the application of advanced convolutional neural networks (CNNs). In pursuit of this goal, we use neural architecture search to automatically explore CNN models for identifying drainage crossings. Our approach not only attains high accuracy (over 97% for average precision) in object detection but also excels in efficiently inferring correct drainage crossings within a remarkably short time frame (0.268 ms). Furthermore, we perform a detailed profiling of our approach on GPU systems to analyze performance bottlenecks.