论文标题
Bose-Einstein冷凝物中的深色孤子：多体物理研究的数据集
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research
论文作者
论文摘要
我们建立了一个超过$ 1.6 \ times10^4 $的数据集，bose-ineinstein冷凝物的实验图像含有孤子激发，以实现机器学习（ML）进行多体物理学研究。该数据集的大约$ 33〜 \％$已手动分配和精心策划的标签。其余部分将使用Soldet自动标记，该物理知识的ML数据分析框架的实现 - 由基于卷积的神经网络网络分类器和OD以及统计学动机的物理知识分类器和质量度量指标组成。该技术说明构成了数据集的确定性参考，为数据科学界提供了开发更复杂的分析工具的机会，以进一步了解非线性多体物理学，甚至推进冷原子实验。
We establish a dataset of over $1.6\times10^4$ experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About $33~\%$ of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and OD as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.
