Space debris is a major problem in space exploration. International bodies continuously monitor a large database of orbiting objects and emit warnings in the form of conjunction data messages. An important question for satellite operators is to estimate when fresh information will arrive so that they can react timely but sparingly with satellite maneuvers. We propose a statistical learning model of the message arrival process, allowing us to answer two important questions: (1) Will there be any new message in the next specified time interval? (2) When exactly and with what uncertainty will the next message arrive? The average prediction error for question (2) of our Bayesian Poisson process model is smaller than the baseline in more than 4 hours in a test set of 50k close encounter events.

翻译：---- 太空碎片是太空探索中的一个主要问题。国际机构不断监视大量的轨道物体数据库，并以联合数据信息警报的形式发出警告。一个重要问题是卫星运营商需要估计新信息何时到达，以便他们可以及时但节省地进行卫星操作。我们提出了一种信息到达过程的统计学习模型，使我们能够回答两个重要问题：（1）在下一个指定的时间间隔内是否会有新的消息？（2）下一条消息何时以及具有什么不确定性到达？在泊松过程贝叶斯模型的问题（2）平均预测误差比50k个接近事件的测试集中的基线小了4个小时以上。