论文标题
一个简单的数据驱动级别查找基于统计异常值检测的量子多体系统的方法
A Simple Data-Driven Level Finding Method of Quantum Many-Body Systems based on Statistical Outlier Detection
论文作者
论文摘要
我们报告了一种简单而纯净的数据驱动方法，可以从观察到的线波长中找到量子多体系统的新能级。在我们的方法中，所有可能的组合都是从未识别线的已知能级和波长中计算出来的。由于每个激发态都表现出许多转变线至不同较低级别的过渡线，因此应从许多级别的组合中重新构建真实水平，而错误的组合则随机分布。这样的巧合可以很容易地从统计上检测到。我们通过在网上提供的未知线列表中找到各种原子和核系统的新水平来证明这种统计方法。
We report a simple and pure data-driven method to find new energy levels of quantum many-body systems only from observed line wavelengths. In our method, all the possible combinations are computed from known energy levels and wavelengths of unidentified lines. As each excited state exhibits many transition lines to different lower levels, the true levels should be reconstructed coincidentally from many level-line combinations, while the wrong combinations distribute randomly. Such a coincidence can be easily detected statistically. We demonstrate this statistical method by finding new levels for various atomic and nuclear systems from unidentified line lists available online.
