调谐到空间频率空间：ZTF警报流中的卫星与空间碎片检测
Tuning into spatial frequency space: Satellite and space debris detection in the ZTF alert stream
AI总结：
针对ZTF警报中卫星及碎片复杂特征漏检问题，本文在ALeRCE分类器中引入差值图像FFT通道，显著提升小视场压缩场景下的检测准确率。
AI中文摘要：
瞬变天体物理现象研究中的一项重大挑战是识别虚假事件，其中地球轨道上的人造卫星和碎片仍是主要污染源。现有流程能够有效识别卫星轨迹，但可能遗漏更复杂的特征，例如成组的卫星闪光。在Rubin Observatory时代，与其前身Zwicky Transient Facility（ZTF）相比，运行规模将增加十倍，因此需要提升分类纯度、改进面向信息丰富警报的数据压缩，并提高流程速度。我们探索将差值图像上的二维快速傅里叶变换（FFT）用作增强卫星检测机器学习模型的工具。以ALeRCE单星图分类器为基线，我们调整其架构，在标准ZTF图像三元组（科学、参考和差值星图）之外加入差值图像FFT的裁剪图。我们评估了若干星图尺寸和分辨率，重点关注因警报大小限制和实时约束而使数据压缩至关重要的情形。加入FFT显著改善了卫星分类，尤其是在最小视场模型（16角秒）中，准确率从72.0±2.9%提升至87.8±1.3%。这证明了FFT在压缩和捕捉扩展卫星特征方面的价值。然而，仅使用FFT无法达到63角秒模型（95.9±1.3%）或多尺度模型（90.6±0.8%）的全上下文性能，这突出了空间上下文的互补作用。我们展示了如何利用FFT从当前和未来时域巡天的警报流中剔除卫星和碎片特征。
英文摘要：
A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, among which human-made satellites and debris in Earth orbit remain major contaminants. Existing pipelines effectively identify satellite trails but can miss more complex signatures, such as collections of satellite glints. In the Rubin Observatory era, the scale of operations will increase tenfold compared to its precursor, the Zwicky Transient Facility (ZTF), requiring improvements in classification purity, data compression for informative alerts, and pipeline speed. We explore the use of the 2D Fast Fourier Transform (FFT) on difference images as a tool to enhance machine learning models for satellite detection. Using the ALeRCE single-stamp classifier as a baseline, we adapt its architecture to incorporate a cutout of the FFT of the difference image alongside the standard ZTF image triplet (science, reference, and difference stamps). We evaluate several stamp sizes and resolutions, focusing on regimes where data compression is critical due to alert size limits and real-time constraints. Incorporating the FFT significantly improves satellite classification, especially in the smallest field-of-view model (16 arcsec), where accuracy increases from 72.0\pm2.9% to 87.8\pm1.3%. This demonstrates the FFT's value in compressing and capturing extended satellite features. However, the FFT alone does not match the full-context performance of the 63 arcsec (95.9\pm1.3%) or multiscale (90.6\pm0.8%) models, highlighting the complementary role of spatial context. We show how FFTs can be leveraged to cull satellite and debris signatures from alert streams in current and future time-domain surveys.
