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Title: Predicting dust emission using galactic 21 cm data

Abstract

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.

Authors:
 [1];  [2];  [2];  [2];  [3]
  1. Univ. of Science and Technology of China, Hefei (China); Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Univ. of Science and Technology of China, Hefei (China)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1598489
Report Number(s):
BNL-213612-2020-JAAM
Journal ID: ISSN 1475-7516; TRN: US2103289
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Cosmology and Astroparticle Physics
Additional Journal Information:
Journal Volume: 2019; Journal Issue: 12; Journal ID: ISSN 1475-7516
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; CMBR experiments; cosmological parameters from CMBR

Citation Formats

Zhang, Guangyu, Chiang, Chi-Ting, Sheehy, Chris, Slosar, Anže, and Wang, Jian. Predicting dust emission using galactic 21 cm data. United States: N. p., 2019. Web. doi:10.1088/1475-7516/2019/12/022.
Zhang, Guangyu, Chiang, Chi-Ting, Sheehy, Chris, Slosar, Anže, & Wang, Jian. Predicting dust emission using galactic 21 cm data. United States. https://doi.org/10.1088/1475-7516/2019/12/022
Zhang, Guangyu, Chiang, Chi-Ting, Sheehy, Chris, Slosar, Anže, and Wang, Jian. Fri . "Predicting dust emission using galactic 21 cm data". United States. https://doi.org/10.1088/1475-7516/2019/12/022. https://www.osti.gov/servlets/purl/1598489.
@article{osti_1598489,
title = {Predicting dust emission using galactic 21 cm data},
author = {Zhang, Guangyu and Chiang, Chi-Ting and Sheehy, Chris and Slosar, Anže and Wang, Jian},
abstractNote = {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.},
doi = {10.1088/1475-7516/2019/12/022},
journal = {Journal of Cosmology and Astroparticle Physics},
number = 12,
volume = 2019,
place = {United States},
year = {Fri Dec 06 00:00:00 EST 2019},
month = {Fri Dec 06 00:00:00 EST 2019}
}

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