Map Reconstruction of radio observations with Conditional Invertible Neural Networks

In radio astronomy, the challenge of reconstructing a sky map from time ordered data (TOD) is known as an inverse problem. Standard map-making techniques and gridding algorithms are commonly employed to address this problem, each offering its own benefits such as producing minimum-variance maps. However, these approaches also carry limitations such as computational inefficiency and numerical instability in map-making and the inability to remove beam effects in grid-based methods. To overcome these challenges, this study proposes a novel solution through the use of the conditional invertible neural network (cINN) for efficient sky map reconstruction. With the aid of forward modeling, where the simulated TODs are generated from a given sky model with a specific observation, the trained neural network can produce accurate reconstructed sky maps. Using the five-hundred-meter aperture spherical radio telescope (FAST) as an example, cINN demonstrates remarkable performance in map reconstruction from simulated TODs, achieving a mean squared error of $2.29\pm 2.14 \times 10^{-4}~\rm K^2$, a structural similarity index of $0.968\pm0.002$, and a peak signal-to-noise ratio of $26.13\pm5.22$ at the $1\sigma$ level. Furthermore, by sampling in the latent space of cINN, the reconstruction errors for each pixel can be accurately quantified.Comment: Accepted for publication in Research in Astronomy and Astrophysics (RAA); 20 pages, 10 figure

Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images -- II: Application of Solid Harmonic Wavelet Scattering Transform

The information regarding how the intergalactic medium is reionized by astrophysical sources is contained in the tomographic three-dimensional 21 cm images from the epoch of reionization. In Zhao et al. (2022a) ("Paper I"), we demonstrated for the first time that density estimation likelihood-free inference (DELFI) can be applied efficiently to perform a Bayesian inference of the reionization parameters from the 21 cm images. Nevertheless, the 3D image data needs to be compressed into informative summaries as the input of DELFI by, e.g., a trained 3D convolutional neural network (CNN) as in Paper I (DELFI-3D CNN). Here in this paper, we introduce an alternative data compressor, the solid harmonic wavelet scattering transform (WST), which has a similar, yet fixed (i.e. no training), architecture to CNN, but we show that this approach (i.e. solid harmonic WST with DELFI) outperforms earlier analyses based on 3D 21 cm images using DELFI-3D CNN in terms of credible regions of parameters. Realistic effects, including thermal noise and residual foreground after removal, are also applied to the mock observations from the Square Kilometre Array (SKA). We show that under the same inference strategy using DELFI, the 21 cm image analysis with solid harmonic WST outperforms the 21 cm power spectrum analysis. This research serves as a proof of concept, demonstrating the potential to harness the strengths of WST and simulation-based inference to derive insights from future 21 cm light-cone image data.Comment: 19 pages, 10 figures, 7 tables. Submitted to ApJ. Comments welcom

Sky reconstruction for the Tianlai cylinder array

In this paper, we apply our sky map reconstruction method for transit type interferometers to the Tianlai cylinder array. The method is based on the spherical harmonic decomposition, and can be applied to cylindrical array as well as dish arrays and we can compute the instrument response, synthesised beam, transfer function and the noise power spectrum. We consider cylinder arrays with feed spacing larger than half wavelength, and as expected, we find that the arrays with regular spacing have grating lobes which produce spurious images in the reconstructed maps. We show that this problem can be overcome, using arrays with different feed spacing on each cylinder. We present the reconstructed maps, and study the performance in terms of noise power spectrum, transfer function and beams for both regular and irregular feed spacing configurations.Comment: 15 pages, 12 figures, accepted by RA

Data Processing Pipeline For Tianlai Experiment

The Tianlai project is a 21cm intensity mapping experiment aimed at detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily affected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline software called {\tt tlpipe} to process the drift scan survey data from the Tianlai experiment. It performs offline data processing tasks such as radio frequency interference (RFI) flagging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhonov regularization for $m$-mode analysis. In this paper we describe the design and implementation of the {\tt tlpipe} and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.Comment: 13 pages, 5 figures, accepted for publication on Astronomy and Computin

Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

In neutral hydrogen (HI) galaxy survey, a significant challenge is to identify and extract the HI galaxy signal from observational data contaminated by radio frequency interference (RFI). For a drift-scan survey, or more generally a survey of a spatially continuous region, in the time-ordered spectral data, the HI galaxies and RFI all appear as regions which extend an area in the time-frequency waterfall plot, so the extraction of the HI galaxies and RFI from such data can be regarded as an image segmentation problem, and machine learning methods can be applied to solve such problems. In this study, we develop a method to effectively detect and extract signals of HI galaxies based on a Mask R-CNN network combined with the PointRend method. By simulating FAST-observed galaxy signals and potential RFI impacts, we created a realistic data set for the training and testing of our neural network. We compared five different architectures and selected the best-performing one. This architecture successfully performs instance segmentation of HI galaxy signals in the RFI-contaminated time-ordered data (TOD), achieving a precision of 98.64% and a recall of 93.59%.Comment: 17 pages, 9 figures, 1 tables. Accepted for publication in RA

The Tianlai Cylinder Pathfinder array: System functions and basic performance analysis

The Tianlai Cylinder Pathfinder is a radio interferometer array designed to test techniques for 21 cm intensity mapping in the post-reionization Universe, with the ultimate aim of mapping the large scale structure and measuring cosmological parameters such as the dark energy equation of state. Each of its three parallel cylinder reflectors is oriented in the north-south direction, and the array has a large field of view. As the Earth rotates, the northern sky is observed by drift scanning. The array is located in Hongliuxia, a radio-quiet site in Xinjiang, and saw its first light in September 2016. In this first data analysis paper for the Tianlai cylinder array, we discuss the sub-system qualification tests, and present basic system performance obtained from preliminary analysis of the commissioning observations during 2016-2018. We show typical interferometric visibility data, from which we derive the actual beam profile in the east-west direction and the frequency band-pass response. We describe also the calibration process to determine the complex gains for the array elements, either using bright astronomical point sources, or an artificial on site calibrator source, and discuss the instrument response stability, crucial for transit interferometry. Based on this analysis, we find a system temperature of about 90 K, and we also estimate the sensitivity of the array