Predicting dust emission using galactic 21 cm data
- Univ. of Science and Technology of China, Hefei (China); Brookhaven National Lab. (BNL), Upton, NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Univ. of Science and Technology of China, Hefei (China)
Understanding large-angular-scale galactic foregrounds is crucial for future CMB experiments aiming to detect B-mode polarization from primordial gravitational waves. Traditionally, the dust component has been separated using its different frequency dependence. However, using non-CMB observations has potential to increase fidelity and decrease the re-construction noise. In this exploratory paper we investigate the capability of galactic 21 cm observations to predict the dust foreground in intensity. We train a neural network to predict the dust foreground as measured by the Planck Satellite from the full velocity data-cube of galactic 21 cm emission as measured by the HI4PI survey. We demonstrate that information in the velocity structure clearly improves the predictive power over both a simple integrated emission model and a simple linear model. The improvement is significant at arc-minute scales but more modest at degree scales. Finally, this proof of principle on temperature data indicates that it might also be possible to improve foreground polarization templates from the same input data.
- Research Organization:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- SC0012704
- OSTI ID:
- 1598489
- Report Number(s):
- BNL-213612-2020-JAAM; TRN: US2103289
- Journal Information:
- Journal of Cosmology and Astroparticle Physics, Vol. 2019, Issue 12; ISSN 1475-7516
- Publisher:
- Institute of Physics (IOP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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