Q-GAIN：用于机器学习和物理信息分析应用的Python包
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
AI总结：
介绍Q-GAIN Python包，它通过模块化工作流实现冷原子实验中的机器学习分类、目标检测和物理信息特征识别，并演示了三个任务：MNIST分类、孤子检测和涡旋识别。
摘要：
这里我们描述了量子气体分析与推理（Q-GAIN）Python包，它能够快速部署机器学习和物理信息分析技术用于冷原子实验。开箱即用，Q-GAIN实现了原子玻色-爱因斯坦凝聚体（BEC）图像中的分类、目标检测和物理信息度量用于特征检测。Q-GAIN鼓励一种自然的、基于模块的工作流：从数据加载和预处理开始，接着是基于机器学习的特征识别，最后是常规分析技术。我们通过为三个机器学习任务配置Q-GAIN来展示这种模块化。首先，我们通过实现MNIST数据集手写数字分类的标准任务来演示Q-GAIN框架的基本工作流。然后，我们在Q-GAIN框架中重新实现了我们早期的孤子检测（SolDet）包，从而能够检测和分析飞行时间数据中的孤子激发。最后，我们开发了一个目标检测工具，用于识别环形BEC图像中的量子化涡旋。
英文摘要：
Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-informed analysis techniques for cold-atom experiments. Out of the box, Q-GAIN implements classification, object detection, and physics-informed metrics for feature detection in images of atomic Bose-Einstein condensates (BECs). Q-GAIN encourages a natural, module-based workflow: starting with data loading and preprocessing, followed by ML-based feature identification, and ending with conventional analysis techniques. We demonstrate this modularity by configuring Q-GAIN for three ML tasks. First, we demonstrate the basic workflow of the Q-GAIN framework by implementing the standard task of classifying handwritten digits from the MNIST dataset. Then, we re-implement our earlier soliton detection (SolDet) package in the Q-GAIN framework, enabling the detection and analysis of solitonic excitations in time-of-flight data. Finally, we develop an object-detection tool that identifies quantized vortices in images of ring-shaped BECs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
