Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

Abstract

The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure (LSS) studies. A major issue for the HI IM survey is to remove the bright foreground contamination. A key to successfully remove the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effect of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future HI IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a preprocessing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA preprocessing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on HI fluctuation amplitude.

13 pages, 13 figures; accepted for publication in MNRAS

References in corpus (23)

• The Clustering of the SDSS DR7 Main Galaxy Sample I: A 4 per cent Distance Measure at z=0.15
• Measurement of 21 cm brightness fluctuations at z ~ 0.8 in cross-correlation
• Late-time cosmology with 21cm intensity mapping experiments
• How accurately can 21 cm tomography constrain cosmology?
• Possibility of Precise Measurement of the Cosmological Power Spectrum With a Dedicated 21cm Survey After Reionization
• Estimating extragalactic Faraday rotation
• HI intensity mapping with MeerKAT: power spectrum detection in cross-correlation with WiggleZ galaxies
• Forecasts on the Dark Energy and Primordial Non-Gaussianity Observations with the Tianlai Cylinder Array
• Fast simulations for intensity mapping experiments
• SKAO HI Intensity Mapping: Blind Foreground Subtraction Challenge
• Impact of Foregrounds on HI Intensity Mapping Cross-Correlations with Optical Surveys
• Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
• Constraining the reionization history using deep learning from 21cm tomography with the Square Kilometre Array
• Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning
• Constraining Polarized Foregrounds for EOR Experiments II: Polarization Leakage Simulations in the Avoidance Scheme
• A direct detection of neutral hydrogen intensity mapping on Mpc scales at $z\approx 0.32$ and $z\approx 0.44$
• Prospects for measuring dark energy with 21 cm intensity mapping experiments: A joint survey strategy
• MeerKAT Primary Beam Measurements in the L Band
• A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs
• The Electromagnetic Characteristics of the Tianlai Cylindrical Pathfinder Array
• The Tianlai dish array low-z surveys forecasts
• Accurate Polarization Calibration at 800 MHz with the Green Bank Telescope
• Deep-Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization

Cited by in corpus (7)

• Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
• Prospects for cosmological research with the FAST array: 21-cm intensity mapping survey observation strategies
• Application of Physics-Informed Neural Networks in Removing Telescope Beam Effects
• FAST Drift Scan Survey for HI Intensity Mapping. II. Stacking-based Beam Construction of the 19-feed Array at $1.4$ GHz
• CMB delensing with deep learning
• Extracting the Epoch of Reionization Signal with 3D U-Net Neural Networks Using Data-driven Systematic Effect Model
• Exploring HI Galaxy Redshift Survey Strategies for the FAST Core Array Interferometry