5 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)…
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…
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…
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…
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…