Abstract
Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the affects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.
Abstract (translated)
现代宽域时域巡天项目通过图像差分和向其社群发送警报来促进对瞬变、可变以及移动现象的研究。过去十年中，机器学习工具已被应用于这些巡天及其前身的数据上，并且从2010年代开始，卷积神经网络（CNN）由于能够直接从输入图像进行预测而被广泛采用。自那时以来，计算机视觉领域持续快速的进步已经改变了使用此类模型的标准实践。如今，使用在大量日常图片（例如ImageNet）上预训练的标准化架构已成为常态。 相比之下，时域天文学研究通常仍会设计定制化的CNN架构，并从头开始进行训练。在此，我们探讨了采用各种预训练方案和标准化模型架构对警报分类性能的影响。我们的发现表明，这些方法所生成的模型可以与专门为过滤警报而设计的定制化、专业化CNN相匹敌甚至超越其表现。此外，我们的结果显示，在Galaxy Zoo中的星系图像上进行预训练通常会比在ImageNet或从头开始训练表现出更好的效果。 我们观察到，标准化架构的设计比定制化的CNN基准线更加优化，尽管可调整参数更多，但在推理时所需的时间和内存却显著减少。随着遗产空间与时间调查及其他图像差分巡天项目的临近，这些发现呼吁了针对警报创建视觉模型的方法发生转变，表明通过采用计算机视觉领域的最新实践，可以实现更高的性能和效率，在时间和数据方面皆如此。
URL
https://arxiv.org/abs/2512.11957
