Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. Some of these transients are the explosive deaths of stars known as supernovae while others are rare, exotic, or entirely new kinds of exciting stellar explosions. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients, making standard approaches of visually identifying new and interesting transients infeasible. To meet this demand, we present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model.
翻译:天文学瞬态是星形物体,在不同时间尺度上暂时变得更亮,并导致在宇宙学和天文学中发现一些最重要的发现。有些瞬态是被称为超新星的恒星爆炸性死亡,而其他恒星则是稀有的、异端的或全新的刺激恒星爆炸。新的天空测量正在观测数量空前的多波长瞬态,使视觉识别新的和有趣的瞬态的标准方法无法实现。为了满足这一需求,我们提出了两种新颖的方法,目的是快速和自动地探测实时的异常瞬时光曲线。这两种方法都基于这样的简单想法:如果已知瞬态群的光曲线可以精确地模拟,那么任何偏离模型预测的情况都可能是不正常的。第一种方法是用时空演网络(TCNs)建造的概率性神经网络,而第二种方法是一种可解释的瞬态贝斯的参数模型。我们展示了神经网络的灵活性,这些网络的属性使得它们成为许多回归任务的强大工具。当我们进行反射时,它们与许多反射任务进行比较时,它们就不太合适。