Closing the stellar labels gap: An unsupervised, generative model for $\textit{Gaia}$ BP/RP spectra
Abstract
The recent release of 220+ million BP/RP spectra in $\textit{Gaia}$ DR3 presents an opportunity to apply deep learning models to an unprecedented number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so massive that no previous spectroscopic survey can provide enough stellar labels to cover the BP/RP parameter space. We present an unsupervised, deep, generative model for BP/RP spectra: a $\textit{scatter}$ variational auto-encoder. We design a non-traditional variational auto-encoder which is capable of modeling both $(i)$ BP/RP coefficients and $(ii)$ intrinsic scatter. Our model learns a latent space from which to generate BP/RP spectra (scatter) directly from the data itself without requiring any stellar labels. We demonstrate that our model accurately reproduces BP/RP spectra in regions of parameter space where supervised learning fails or cannot be implemented.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2023
- DOI:
- 10.48550/arXiv.2307.06378
- arXiv:
- arXiv:2307.06378
- Bibcode:
- 2023arXiv230706378L
- Keywords:
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- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Solar and Stellar Astrophysics
- E-Print:
- Accepted at the ICML 2023 Workshop on Machine Learning for Astrophysics. 6 pages, 3 figures