Foreground Removal of CO Intensity Mapping Using Deep Learning

Abstract

Line intensity mapping (LIM) is a promising probe to study star formation, the large-scale structure of the Universe, and the epoch of reionization (EoR). Since carbon monoxide (CO) is the second most abundant molecule in the Universe except for molecular hydrogen ${\mathrm{H}}_2$, it is suitable as a tracer for LIM surveys. However, just like other LIM surveys, CO intensity mapping also suffers strong foreground contamination that needs to be eliminated for extracting valuable astrophysical and cosmological information. In this work, we take ${}^{12}$CO($J$=1-0) emission line as an example to investigate whether deep learning method can effectively recover the signal by removing the foregrounds. The CO(1-0) intensity maps are generated by N-body simulations considering CO luminosity and halo mass relation, and we discuss two cases with median and low CO signals by comparing different relations. We add foregrounds generated from real observations, including thermal dust, spinning dust, free-free, synchrotron emission and CMB anisotropy. The beam with sidelobe effect is also considered. Our deep learning model is built upon ResUNet, which combines image generation algorithm UNet with the state-of-the-art architecture of deep learning, ResNet. The principal component analysis (PCA) method is employed to preprocess data before feeding it to the ResUNet. We find that, in the case of low instrumental noise, our UNet can efficiently reconstruct the CO signal map with correct line power spectrum by removing the foregrounds and recovering PCA signal loss and beam effects. Our method also can be applied to other intensity mappings like neutral hydrogen 21cm surveys.

12 pages, 12 figures, 2 tables. Accepted for publication in MNRAS

References in corpus (15)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Conditional Generative Adversarial Nets
• Generative Adversarial Networks
• Star formation relations and CO SLEDs across the J-ladder and redshift
• Line-Intensity Mapping: 2017 Status Report
• Blind foreground subtraction for intensity mapping experiments
• Molecular gas content in strongly-lensed z~1.5-3 star-forming galaxies with low IR luminosities
• Intensity mapping of [CII] from early galaxies
• An Intensity Mapping Detection of Aggregate CO Line Emission at 3 mm
• Intensity mapping of H-alpha, H-beta, [OII] and [OIII] lines at z<5
• Measuring Galaxy Clustering and the Evolution of [CII] Mean Intensity with far-IR Line Intensity Mapping During 0.5 < z < 1.5
• From Dark Matter to Galaxies with Convolutional Networks
• Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning
• The Electromagnetic Characteristics of the Tianlai Cylindrical Pathfinder Array
• Forecasting the Cross-Correlation of the CSST galaxy survey with the FAST HI Intensity Map

Cited by in corpus (2)

• Constraining the [CII] luminosity function from the power spectrum of line-intensity maps at redshift 3.6
• Probing the warm dark matter mass with [C II] intensity mapping