We present a framework to compose artificial neural networks in cases where the data cannot be treated as independent events. Our particular motivation is star galaxy classification for ground based optical surveys. Due to a turbulent atmosphere and imperfect instruments, a single image of an astronomical object is not enough to definitively classify it as a star or galaxy. Instead the context of the surrounding objects imaged at the same time need to be considered in order to make an optimal classification. The model we present is divided into three distinct ANNs: one designed to capture local features about each object, the second to compare these features across all objects in an image, and the third to make a final prediction for each object based on the local and compared features. By exploiting the ability to replicate the weights of an ANN, the model can handle an arbitrary and variable number of individual objects embedded in a larger exposure. We train and test our model on simulations of a large up and coming ground based survey, the Large Synoptic Survey Telescope (LSST). We compare to the state of the art approach, showing improved overall performance as well as better performance for a specific class of objects that is important for the LSST.
Noble Kennamer (University of California, Irvine)
University of California David Kirkby (University of California, Irvine)
Alex Ihler (UC Irvine)
University of California Francisco Javier Sanchez-Lopez (University of California, Irvine)
Related Events (a corresponding poster, oral, or spotlight)
2018 Oral: ContextNet: Deep learning for Star Galaxy Classification »
Thu Jul 12th 03:00 -- 03:20 PM Room Victoria