论文标题
幽灵：仅使用主机星系信息准确地关联和区分超新星
GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
论文作者
论文摘要
我们介绍了Ghost，这是一个16,175个光谱分类的超新星的数据库及其宿主星系的性能。我们已经使用图像梯度开发了一种宿主星系关联方法，该方法比以前的方法比以前的方法更少，对低Z宿主的失误和高Z宿主的完整性更高。我们使用降低尺寸降低来识别区分超新星类的宿主星系属性。我们的结果表明，可以使用宿主亮度信息和从宿主的光谱中得出的宿主亮度信息和扩展度措施来分离SLSNE，SNE IA和CORE COLLAPSE SUPERNOVAE的宿主。接下来，我们使用Ghost的数据训练随机的森林模型，以使用独家托管星系信息和超新星的径向偏移来预测超新星类。我们可以以〜70％的精度区分SNE IA和核心崩溃超新星，而无需事件本身的任何光度数据。 Vera C. Rubin天文台将迎来瞬时人群研究的新时代，要求改进光度工具，以快速识别和分类瞬时事件。通过识别具有高歧视能力的宿主特征，我们将保持SN样品纯度，并继续随着数据量增加而确定科学相关的事件。 Ghost数据库和我们用于将瞬变与主机星系关联的相应软件均可公开使用。
We present GHOST, a database of 16,175 spectroscopically classified supernovae and the properties of their host galaxies. We have developed a host galaxy association method using image gradients that achieves fewer misassociations for low-z hosts and higher completeness for high-z hosts than previous methods. We use dimensionality reduction to identify the host galaxy properties that distinguish supernova classes. Our results suggest that the hosts of SLSNe, SNe Ia, and core collapse supernovae can be separated using host brightness information and extendedness measures derived from the host's light profile. Next, we train a random forest model with data from GHOST to predict supernova class using exclusively host galaxy information and the radial offset of the supernova. We can distinguish SNe Ia and core collapse supernovae with ~70% accuracy without any photometric data from the event itself. Vera C. Rubin Observatory will usher in a new era of transient population studies, demanding improved photometric tools for rapid identification and classification of transient events. By identifying the host features with high discriminatory power, we will maintain SN sample purities and continue to identify scientifically relevant events as data volumes increase. The GHOST database and our corresponding software for associating transients with host galaxies are both publicly available.
