What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction

1 code implementation • 14 Mar 2022 • Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu, The LSST Dark Energy Science Collaboration

We present a study of the potential for Convolutional Neural Networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" classification without requiring a template subtracted (or difference) image which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data.

Feature Engineering Task 2