GHOST：仅使用宿主星系信息精确关联和区分超新星
GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
摘要：
我们介绍了GHOST，一个包含16，175颗光谱分类超新星及其宿主星系属性的数据库。我们已经开发了一个主机星系协会的方法，使用图像梯度，实现低Z主机和高Z主机比以前的方法更高的完整性更少的误关联。我们使用降维来识别区分超新星类的宿主星系属性。我们的研究结果表明，主机SLSNe，SNe Ia和核心塌陷超新星可以分离主机亮度信息和扩展性措施来自主机的光剖面。接下来，我们用GHOST的数据训练一个随机森林模型来预测超新星的类别，只使用宿主星系的信息和超新星的径向偏移。我们可以区分SNe Ia和核心坍缩超新星，准确率约为70%，而无需事件本身的任何光度数据。维拉角鲁宾天文台将迎来一个新的时代，短暂的人口研究，需要改进的光度工具，快速识别和分类的短暂事件。通过识别具有高区分能力的宿主特征，我们将保持SN样本纯度，并随着数据量的增加继续识别科学相关事件。GHOST数据库和我们相应的软件相关联的瞬变与宿主星系都是公开的。
英文摘要：
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.
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