GS-TEC：Gaia分光光度瞬态事件分类器
GS-TEC: the Gaia Spectrophotometry Transient Events Classifier
摘要：
我们提出了一种算法，用于分类附近的瞬态物体探测到的盖亚卫星。该算法将使用卫星上的蓝色和红色分光光度计的低分辨率光谱。采用贝叶斯方法，我们使用新构建的参考光谱库和文献驱动的先验知识对光谱进行建模。我们发现，在盖亚$G$的亮度超过19的幅度，约75%的瞬态将被稳健地分类。对于幅度$G\leq$ 18，用于SNe类型I的算法的效率高于80%，在幅度$G$ =19时下降到大约60%。对于II型SNe，对于$G\leq$ 18，效率在75 - 60%之间变化，在$G$ =19时降至50%。我们的分类器的纯度是约95%的SNe I型的所有幅度。对于SNe类型II，对于具有$G \leq$ 19的对象，其超过90%。GS-TEC还估算了SNe I和SNe II的红移，误差为$σ_z \le$ 0.01，历元的不确定度分别为$σ_t \simeq$ 13和32天。GS-TEC被设计用于部分校准的盖亚数据。然而，这个概念可以扩展到其他类型的低分辨率光谱分类进行中的调查。
英文摘要：
We present an algorithm for classifying the nearby transient objects detected by the Gaia satellite. The algorithm will use the low-resolution spectra from the blue and red spectro-photometers on board of the satellite. Taking a Bayesian approach we model the spectra using the newly constructed reference spectral library and literature-driven priors. We find that for magnitudes brighter than 19 in Gaia $G$ magnitude, around 75\% of the transients will be robustly classified. The efficiency of the algorithm for SNe type I is higher than 80\% for magnitudes $G\leq$18, dropping to approximately 60\% at magnitude $G$=19. For SNe type II, the efficiency varies from 75 to 60\% for $G\leq$18, falling to 50\% at $G$=19. The purity of our classifier is around 95\% for SNe type I for all magnitudes. For SNe type II it is over 90\% for objects with $G \leq$19. GS-TEC also estimates the redshifts with errors of $σ_z \le$ 0.01 and epochs with uncertainties $σ_t \simeq$ 13 and 32 days for type SNe I and SNe II respectively. GS-TEC has been designed to be used on partially calibrated Gaia data. However, the concept could be extended to other kinds of low resolution spectra classification for ongoing surveys.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
