A Deep Learning Approach to Large-Scale Light Curve Prediction and Real-Time Anomaly Detection with Grubbs Criterion
Document Type
Conference Proceeding
Publication Date
10-30-2020
Abstract
In light curves (LCs), the brightness of stars is associated with time, and so is its image. The traditional data processing methods cannot effectively handle real-time and large-volume data of various LCs. To address this issue, this work develops a deep neural network named Dropout-based Recurrent Neural Networks (DRNN). It extracts complicated features of all images captured by Mini Ground-based Wide-Angle Camera array (Mini-GWAC) for point source extraction and cross-certification through Long Short-Term Memory units. DRNN can also produce warnings for abnormal values of light change curves. Furthermore, this work optimizes the training model by combining a dropout method with an adaptive moment estimation algorithm to iteratively update the network weight of the RNN based on the LCs data. Extensive experiments with a Mini-GWAC dataset demonstrate that DRNN outperforms several typical methods in terms of prediction performance of star brightness in large-scale astronomical LCs.
Identifier
85096351662 (Scopus)
ISBN
[9781728168531]
Publication Title
2020 IEEE International Conference on Networking Sensing and Control Icnsc 2020
External Full Text Location
https://doi.org/10.1109/ICNSC48988.2020.9238105
Grant
61703011
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Huang, Xiaodong; Peng, Lei; Lu, Cheng; Bi, Jing; and Yuan, Haitao, "A Deep Learning Approach to Large-Scale Light Curve Prediction and Real-Time Anomaly Detection with Grubbs Criterion" (2020). Faculty Publications. 4901.
https://digitalcommons.njit.edu/fac_pubs/4901
