Junhua Gu

TL;DR: In this article, the authors re-examine the separation approaches based on the quadratic polynomial fitting technique in frequency space to investigate whether they work satisfactorily with complex foreground by quantitatively evaluating the quality of restored 21 cm signals in terms of sample statistics.

TL;DR: In this article, the authors presented a catalog of 624 radio sources detected around the North Celestial Pole (NCP) with the 21 Centimeter Array (21CMA), a radio interferometer dedicated to the statistical measurement of the epoch of reionization (EoR).

TL;DR: This paper proposes a novel deep-learning-based method that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal and concludes that, by hierarchically learning sophisticated features through multiple Convolutional layers, the CDAE is a powerful tool that can be used to overcome the complicated beam effects and accurately separate the NLP signal.

TL;DR: The giant radio array for neutrino detection (GRAND) project as discussed by the authors was proposed to detect high-energy neutrinos via the measurement of air showers induced by the decay in the atmosphere of leptons produced by the interaction of the cosmic neutrons under the Earth surface.

TL;DR: TREND50 as mentioned in this paper is a radio detection setup of 50 self-triggered antennas working in the 50-100 MHz frequency range and deployed in a radio-quiet valley of the Tianshan mountains (China).