Galaxy Morphology Classification Using Bayesian Neural Networks for LSST

• DOI: 10.2172/1969686
• Publication Date: 2023-04-18T02:20:42Z
• Authors: Marina Dunn; Aleksandra Ciprij...; Brian Nord; Bahram Mobasher

Within the decade, many new ground and space-based observatories will become operational, generating massive amounts of data on short timescales. New surveys like Rubin Observatory's Legacy Survey Space Time (LSST) be capable observing objects with greater resolution than ever before, but processing analyzing these datasets optimally pose a significant challenge. In an effort to prepare for this, we explore how incorporating Deep Neural Networks can better support future data-intensive Astrophysics tasks such as galaxy morphology classification.