Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models. In this work, we study Domain Adaptation (DA) for real/bogus classification of astronomical alerts using four different datasets: HiTS, DES, ATLAS, and ZTF. We study the domain shift between these datasets, and improve a naive deep learning classification model by using a fine tuning approach and semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced accuracy of these models for different source-target scenarios. We find that both the fine tuning and MME models improve significantly the base model with as few as one labeled item per class coming from the target dataset, but that the MME does not compromise its performance on the source dataset.

该研究在时域天文学中使用多个大规模数据集实时进行分析，开发了多源流的机器学习模型。研究者通过研究HiTS、DES、ATLAS和ZTF等四个不同数据集之间的领域转换，通过精细调整和半监督深度领域自适应的Minimax Entropy (MME)方法改进了一种纯粹深度学习分类模型的性能。通过对不同源目标场景的模型进行平衡准确性比较，发现即便仅使用目标数据集中每类一个标记样本，精细调整和MME模型都能显著提高基础模型的性能，而MME模型不会影响源数据集上的性能。

基于极小极大熵的领域自适应方法用于天文警报的真伪分类