6 papers
Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization
Anshuman Acharya, Michele Bianco, Daniela Breitman +18
When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR)…
SwinYNet: A Transformer-based Multi-Task Model for Accurate and Efficient FRB Search
Yunchuan Chen, Shulei Ni, Chan Li +11
In this study, we present a transformer-based multi-task model for Fast Radio Burst (FRB) detection, signal segmentation, and parameter estimation directly from time-frequency data…
Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation
Zitong Wang, Feng Shi, Le Zhang +5
Twenty-one-centimeter intensity mapping is a powerful probe of the large-scale distribution of neutral hydrogen (HI) and cosmological observables such as baryon acoustic oscillatio…
Application of Physics-Informed Neural Networks in Removing Telescope Beam Effects
Shulei Ni, Yisheng Qiu, Yunchuan Chen +5
This study introduces {\tt{PI-AstroDeconv}}, a physics-informed semi-supervised learning method specifically designed for removing beam effects in astronomical telescope observatio…
PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
Shulei Ni, Yisheng Qiu, Yunchun Chen +4
In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and…
Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method
Zihao Song, Huaxi Chen, Donghui Quan +5
Identifying neutral hydrogen (\hi) galaxies from observational data is a significant challenge in \hi\ galaxy surveys. With the advancement of observational technology, especially…