Implementing the LSST Software Stack for DESGW processing and the Integration of Convolutional Neural Networks into the DESGW Pipeline
Year: 2022
Type: article
Abstract: The Dark Energy Survey Gravitational Wave (DESGW) group strives to understand thelargescale structure of the universe and galaxies by looking for electromagnetic signatures intelescope images following gravitational wave detections. The DESGW group uses a processing pipeline to perform difference imaging to look for potential candidates. In order to perform these searches more effectively, we first explored using an alternative processing pipeline, the LSST Software Stack to analyze the telescope images. Because the LSST software stack is currently transitioning between its generation 2 and generation 3 system, we decided that while the software will be usable in the future once generation 3 is complete, at the moment its incompleteness makes it impractical to use. We then decided to work on improving the current pipeline by developing a module that integrates a Convolutional Neural Network (CNN) to test difference imaging products for bad subtractions due to misalignment.
Author: Navarro, Alexander [Andrews U.]
Institution: Andrews University
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4287438779