基于深度学习目标检测算法的光变曲线信号识别。II：一个通用的光变曲线分类框架
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
AI总结：
提出基于弱监督目标检测的深度学习框架，自动提取光变曲线时频域特征，在开普勒、TESS和兹维基瞬变设施数据上达87%准确率，并支持直接迁移和异常检测。
摘要：
各类项目产生了海量的天文测光数据，识别变星和其他天体类别需要付出大量努力。鉴于此，一个通用且广泛适用的分类框架将简化针对不同天文天体设计特定分类器的过程。我们提出了一种新颖的深度学习框架，利用弱监督目标检测模型对光变曲线进行分类。我们的框架自动确定光变曲线和功率谱的最佳窗口，并放大其对应数据。这使得能够从时域和频域自动提取特征，使我们的模型能够处理不同尺度和采样间隔的数据。我们在从开普勒（Kepler）、凌日系外行星勘测卫星（TESS）和兹威基瞬变设施（Zwicky Transient Facility）的多波段观测中获得的变星和瞬变源数据集上训练模型。对于组合的变星和瞬变事件，我们达到了87%的准确率，与以往基于特征的模型性能相当。我们的训练模型可直接用于其他任务，如全天自动超新星巡天（All-sky Automated Survey for Supernovae），无需任何重新训练或微调。为解决预测概率校准不当的已知问题，我们应用共形预测生成稳健的预测集，保证以给定概率覆盖真实标签。此外，我们整合了多种异常检测算法，使模型具备识别分布外天体的能力。我们的框架已在Deep-LC工具包中实现，这是一个开源的Python包，托管在Github（https://github.com/ckm3/Deep-LC）和PyPI上。
英文摘要：
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework would simplify the process of designing specific classifiers for various astronomical objects. We present a novel deep learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on data sets obtained from Kepler, TESS, and Zwicky Transient Facility multiband observations of variable stars and transients. We achieve an accuracy of 87% for combined variables and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly for other missions, such as the All-sky Automated Survey for Supernovae, without requiring any retraining or fine-tuning. To address known issues with miscalibrated predictive probabilities, we apply conformal prediction to generate robust predictive sets that guarantee true-label coverage with a given probability. Additionally, we incorporate various anomaly detection algorithms to empower our model with the ability to identify out-of-distribution objects. Our framework is implemented in the Deep-LC toolkit, which is an open-source Python package hosted on Github (https://github.com/ckm3/Deep-LC) and PyPI.
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。
