Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks

Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks

We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo- z ) estimation. Photo- z uncertainty estimates are critical for the science goals of upcoming large-scale surveys such as the...

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Bibliographic Details
Main Authors: Evan Jones, Tuan Do, Bernie Boscoe, Jack Singal, Yujie Wan, Zooey Nguyen
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:The Astrophysical Journal
Subjects:
Cosmology
Neural networks
Bayesian statistics
Galaxy distances
Redshift surveys
Galaxy photometry
Online Access:https://doi.org/10.3847/1538-4357/ad2070

Internet

https://doi.org/10.3847/1538-4357/ad2070

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