Accelerating Stellar Photometric Distance Estimates with Neural Networks
Building on the Bayesian approach to estimating stellar distances from broadband photometry, we show that the computation can be accelerated by about an order of magnitude by using neural networks. Focusing on the case of the ugrizy filter complement for Rubin’s Legacy Survey of Space and Time (LSST...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-3881/addf51 |
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