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18 January 2025 |
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Article overview
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Stellar Karaoke: Deep Blind Separation of Terrestrial Atmospheric Effects out of Stellar Spectra by Velocity Whitening | | Nima Sedaghat
; J. Bryce Kalmbach
; Brianna M. Smart
; Erin L. Howard
; | | Date: |
1 Jan 2023 | | Abstract: | We exploit the statistical independence of stellar features and atmospheric
adversarial effects in stellar spectra, to remove the latter from observed
signals using a fully unsupervised data-driven approach. Concretely, we first
increase the inter-observation entropy of telluric absorption lines by imposing
a random, virtual radial velocity to the observed spectrum. This novel "trick"
results in a non-standard form of "whitening" in the atmospheric components of
the spectrum, decorelating them across multiple observations. Then we use deep
convolutional auto-encoders, to learn a feature-space in which the two
"sources" of information, stellar and atmospheric, are easily separable,
leading to removal of the latter. We apply the process on spectra from two
different data collections: ~250,000 HARPS spectra and ~660,000 from SDSS. We
compare and analyze the results across datasets, as well as with existing
tools, and discuss directions for utilizing the introduced method as a fast and
more reliable tool in the future. | | Source: | arXiv, 2301.00313 | | Services: | Forum | Review | PDF | Favorites | |
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