论文标题
使用卷积神经网络识别黑暗能源调查中的瞬变
Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks
论文作者
论文摘要
通过图像差异发现新瞬态而无需直接人类干预的能力是观察天文学的重要任务。对于这类图像分类问题，机器学习技术（例如卷积神经网络（CNN））表现出了很大的成功。在这项工作中，我们介绍了来自Dark Energy Survey Supernova计划（DES-SN）的CNN对图像的自动瞬态识别的结果，其主要重点是使用IA型超新星用于宇宙学。通过对CNN进行架构搜索，我们从人工制品（图像缺陷，错误分配等）中确定了有效选择非艺术的网络（例如，超新星，可变星，AGN等），从而实现了先前与随机森林所做的工作效率，而无需花费任何努力，而无需花费任何功能识别。 CNN还可以帮助我们确定一个标签错误的图像的子集。在此子集中对图像进行重新标记，与CNN的结果分类明显优于以前的结果。
The ability to discover new transients via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient identification on images with CNNs for an extant dataset from the Dark Energy Survey Supernova program (DES-SN), whose main focus was on using Type Ia supernovae for cosmology. By performing an architecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g. supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results.
