Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries from noisy and imperfect data without the knowledge of the order parameter. Here, we apply different unsupervised machine learning techniques, including anomaly detection and influence functions, to experimental data from ultracold atoms. In this way, we obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. We show that these methods can successfully be applied to experimental data at finite temperatures and to the data of Floquet systems when post-processing the data to a single micromotion phase. Our work provides a benchmark for the unsupervised detection of new exotic phases in complex many-body systems.

识别相变是量子多体物理学的主要挑战之一。最近，机器学习方法已被证明是一种在不知道顺序参数的情况下从嘈杂和不完美的数据中定位相边界的替代方法。在这里，我们将不同的无监督机器学习技术（包括异常检测和影响函数）应用于来自超冷原子的实验数据。通过这种方式，我们以完全无偏的方式获得了 Haldane 模型的拓扑相图。我们表明，当将数据后处理到单个微动阶段时，这些方法可以成功地应用于有限温度下的实验数据和 Floquet 系统的数据。我们的工作为复杂多体系统中新奇特相的无监督检测提供了一个基准。