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DOE OSTI · 1969686

Galaxy Morphology Classification Using Bayesian Neural Networks for LSST

Dunn, Marina M.·Ciprijanovic, Aleksandra Miodrag·Nord, Brian·Mobasher, Bahram
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Abstract

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 of Space and Time (LSST) will be capable of observing objects with greater resolution than ever before, but processing and analyzing these datasets optimally will 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.

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BibTeXRIS

Dunn, Marina M., Ciprijanovic, Aleksandra Miodrag, Nord, Brian, Mobasher, Bahram. 2023-03-30. Galaxy Morphology Classification Using Bayesian Neural Networks for LSST. https://doi.org/10.2172/1969686

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