arXiv 2508.034002025-08-05astro-ph.IM

DeepAP：基于深度学习的孔径测光可行性评估与孔径大小预测

DeepAP: Deep Learning-based Aperture Photometry Feasibility Assessment and Aperture Size Prediction

Zheng-Jun Du, Qing-Quan Li, Yi-Cheng Rui, Yu-Li Liu, Yu-Ting Wu, Dong Li, Bing-Feng Seng, Yi-Fan Xuan, Fa-Bo Feng

备注：: 16 pages,12 figures

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AI总结：

本文提出DeepAP，一个两阶段深度学习框架，利用ViT评估孔径测光可行性并用ResNet预测最佳孔径大小，显著提升精度与速度，为高精度巡天项目自动应用孔径测光奠定基础。

摘要：

孔径测光是一种基础技术，广泛用于天宇等光学巡天项目中获取高精度光变曲线。然而，其在拥挤场中的有效性受限，且孔径大小的选择对测光精度有至关重要的影响。为解决这些挑战，我们提出DeepAP，一个高效且准确的两阶段深度学习框架，用于孔径测光。具体而言，对于给定源，我们首先训练一个视觉变换器（ViT）模型来评估其孔径测光的可行性。随后，我们训练残差神经网络（ResNet）来预测其最佳孔径大小。在孔径测光可行性评估方面，ViT模型在测试集上取得了0.96的ROC AUC值，精确率为0.974，召回率为0.930，F1分数为0.952。在孔径大小预测方面，ResNet模型通过自适应选择适合不同亮度源的孔径，有效缓解了经典增长曲线方法固有的偏差，从而在广泛的目标范围内提高了信噪比（SNR）。同时，由于孔径大小估计的粒度更细，测试集中的一些样本获得了比穷举孔径大小枚举更高的信噪比。通过将ResNet与ViT网络集成，DeepAP框架对一批10张图像的处理时间中位数为18毫秒，相比穷举孔径大小枚举，速度提升了约59000倍。这项工作为未来高精度巡天项目（如天宇和LSST）中孔径测光的自动应用铺平了道路。源代码和模型可在https://github.com/ruiyicheng/DeepAP获取。

英文摘要：

Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu. However, its effectiveness is limited in crowded fields, and the choice of aperture size critically impacts photometric precision. To address these challenges, we propose DeepAP, an efficient and accurate two-stage deep learning framework for aperture photometry. Specifically, for a given source, we first train a Vision Transformer (ViT) model to assess its feasibility of aperture photometry. We then train the Residual Neural Network (ResNet) to predict its optimal aperture size. For aperture photometry feasibility assessment, the ViT model yields an ROC AUC value of 0.96, and achieves a precision of 0.974, a recall of 0.930, and an F1 score of 0.952 on the test set. For aperture size prediction, the ResNet model effectively mitigates biases inherent in classical growth curve methods by adaptively selecting apertures appropriate for sources of varying brightness, thereby enhancing the signal-to-noise ratio (SNR) across a wide range of targets. Meanwhile, some samples in the test set have a higher SNR than those obtained by exhaustive aperture size enumeration because of the finer granularity of aperture size estimation. By integrating ResNet with the ViT network, the DeepAP framework achieves a median total processing time of 18 milliseconds for a batch of 10 images, representing a speed-up of approximately 59000 times compared to exhaustive aperture size enumeration. This work paves the way for the automatic application of aperture photometry in future high-precision surveys such as Tianyu and LSST. The source code and model are available at https://github.com/ruiyicheng/DeepAP.

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