论文标题
用于自动分类的包含超新星灯回波的图像的包装
A Package for the Automated Classification of Images Containing Supernova Light Echoes
论文作者
论文摘要
语境。可以在天文学差异图像中检测到超新星的所谓“光回声” - 爆发呈爆发的星际灰尘的明显运动。但是，光回声极为罕见，这使手动检测成为艰巨的任务。数百年历史的超新星光回声的调查可能涉及数百个宽视野成像器的点，其中每个CCD放大器的子图像都需要检查。目标。我们介绍了ALED，这是一个实现（i）胶囊网络的Python软件包，该胶囊网络训练有素，可以自动识别具有至少一个超新星光回波的概率，以及（ii）路由路径可视化以定位于识别图像中的光回声和/或光相式的特征。方法。我们将ALED（ALED-M）实现的胶囊网络与不同体系结构的几个胶囊和卷积神经网络进行比较。我们还将ALED应用于大量的天文差异图像目录，并手动检查候选光回声图像以进行人类验证。结果。发现ALED-M在测试集上达到90％的分类精度，并通过路由路径可视化精确定位已识别的光回波。从一组13,000多个天文图像中，ALED确定了一组在手动分类中被忽略的光回声。 ALED可通过github.com/lightechodetection/aled获得。
Context. The so-called "light echoes" of supernovae - the apparent motion of outburst-illuminated interstellar dust - can be detected in astronomical difference images; however, light echoes are extremely rare which makes manual detection an arduous task. Surveys for centuries-old supernova light echoes can involve hundreds of pointings of wide-field imagers wherein the subimages from each CCD amplifier require examination. Aims. We introduce ALED, a Python package that implements (i) a capsule network trained to automatically identify images with a high probability of containing at least one supernova light echo, and (ii) routing path visualization to localize light echoes and/or light echo-like features in the identified images. Methods. We compare the performance of the capsule network implemented in ALED (ALED-m) to several capsule and convolutional neural networks of different architectures. We also apply ALED to a large catalogue of astronomical difference images and manually inspect candidate light echo images for human verification. Results. ALED-m, was found to achieve 90% classification accuracy on the test set, and to precisely localize the identified light echoes via routing path visualization. From a set of 13,000+ astronomical images, ALED identified a set of light echoes that had been overlooked in manual classification. ALED is available via github.com/LightEchoDetection/ALED.
