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arXiv 0810.4945astro-ph
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天气巡天中目标分类的新方法

New Approaches to Object Classification in Synoptic Sky Surveys

C. Donalek, A. Mahabal, S. G. Djorgovski, S. Marney, A. Drake, E. Glikman, M. J. Graham, R. Williams

摘要:

数字天气天空调查提出了几个新的对象分类的挑战。在瞬时事件的实时检测和分类是科学驱动力的调查中,需要有效消除与仪器相关的伪影,这些伪影可能伪装成检测管道中的瞬时源,例如,未移除的大宇宙射线、饱和拖尾、反射、串扰伪影等。我们已经使用监督神经网络为Palomar-Quest(PQ)调查中的实时处理管道实现了这样的滤波器。在训练阶段之后,对于每个对象,它将一组测量的形态参数作为输入,并返回它是真实对象的概率。尽管许多种类的伪影的训练案例数量相对较少,但总体伪影分类率约为90%,在我们的实时扫描期间没有真正的瞬态错误分类。另一个问题是如何在多通道巡天中分配最佳的恒星-星系分类,其中视宁度和其他条件在不同时期之间发生变化,可能会对同一物体产生不一致的分类。我们已经实现了一个恒星/星系的多通道分类器,利用外部和先验知识,找到最佳的分类,从个别派生的。这两种技术都可以应用于其他类似的调查和数据集。

英文摘要:

Digital synoptic sky surveys pose several new object classification challenges. In surveys where real-time detection and classification of transient events is a science driver, there is a need for an effective elimination of instrument-related artifacts which can masquerade as transient sources in the detection pipeline, e.g., unremoved large cosmic rays, saturation trails, reflections, crosstalk artifacts, etc. We have implemented such an Artifact Filter, using a supervised neural network, for the real-time processing pipeline in the Palomar-Quest (PQ) survey. After the training phase, for each object it takes as input a set of measured morphological parameters and returns the probability of it being a real object. Despite the relatively low number of training cases for many kinds of artifacts, the overall artifact classification rate is around 90%, with no genuine transients misclassified during our real-time scans. Another question is how to assign an optimal star-galaxy classification in a multi-pass survey, where seeing and other conditions change between different epochs, potentially producing inconsistent classifications for the same object. We have implemented a star/galaxy multipass classifier that makes use of external and a priori knowledge to find the optimal classification from the individually derived ones. Both these techniques can be applied to other, similar surveys and data sets.

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。

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