Imene Goumiri, Lawrence Livermore National Laboratory; Amanda Muyskens, Lawrence Livermore National Laboratory (LLNL); Michael Schneider, Lawrence Livermore National Laboratory; Benjamin Priest, Lawrence Livermore National Laboratory; Robert Armstrong, Lawrence Livermore National Laboratory
Keywords: Stag-Galaxy, Gaussian Processes, Model Reduction, Machine Learning, Classification
Abstract:
A problem of interest in astronomy is the automatic classification of images from modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey to separate distant galaxies from dim stars in our own galaxy when both look like low-resolution point sources. Recently, this star-galaxy separation challenge has been approached with Deep Neural Networks (DNN) which are good at learning complex nonlinear embeddings. Gaussian Processes (GPs), which infer posterior distributions over functions and naturally quantify uncertainty, havent been a tool of choice for this task mainly because popular kernels exhibit limited expressivity on complex and high-dimensional data.
In this paper, we present a novel approach to the star-galaxy separation problem that uses GPs and reap their benefits while solving many of the issues traditionally affecting them for classification of high-dimensional data. After an initial filtering of the raw data of star and galaxy image cutouts, we first reduce the dimensionality of the input images by using a Principal Components Analysis (PCA) before applying GPs using a simple Radial Basis Function (RBF) kernel on the reduced data. Using this method, we greatly improve the accuracy of the classification over a basic application of GPs while improving the computational efficiency and scalability of the method.
Date of Conference: September 15-18, 2020
Track: Machine Learning Applications of SSA