activity
20232026
most citedExtracting the Epoch of Reionization Signal with 3D U-Net Neural Networks Using Data-driven Systematic Effect Model

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.CO2026

Foreground Characterization and Mitigation in the Observations of the CD/EoR with the SKA

Philip Bull, Jacob Burba, Emilio Ceccotti +20

The Square Kilometre Array (SKA), with its unprecedented sensitivity, frequency coverage, and large collecting area, is poised to revolutionize our understanding of the Cosmic Dawn…

astro-ph.CO2026

Overview of 21cm Experiments at high redshift with SKAO

CD Science Working Group, Gianni Bernardi, Daniela Breitman +81

We provide an overview of the eight SKAO Science Book chapters that motivate the Epoch of Reionisation and Cosmic Dawn experiments with SKA-Low. We describe the individual SKA-Low…

astro-ph.IM2026

Observations of the Cosmic Dawn and Epoch of Reionization with the SKAO: Observational Lessons Learned from Precursors and Pathfinder Instruments

Eloy de Lera Acedo, James Aguirre, Dominic Anstey +34

This chapter summarizes the observational lessons learned after two decades of observations of the Cosmic Dawn (CD) and Epoch of Reionization (EoR) with SKAO pathfinders and precur…

astro-ph.IM2024★ 2 cited

Extracting the Epoch of Reionization Signal with 3D U-Net Neural Networks Using Data-driven Systematic Effect Model

Li-Yang Gao, Léon V. E. Koopmans, Florent G. Mertens +11

Neutral hydrogen (HI) serves as a crucial probe for the Cosmic Dawn and the Epoch of Reionization (EoR). Actual observations of the 21-cm signal often encounter challenges such as…

astro-ph.CO2023

21-cm Signal from the Epoch of Reionization: A Machine Learning upgrade to Foreground Removal with Gaussian Process Regression

Anshuman Acharya, Florent Mertens, Benedetta Ciardi +7

In recent years, a Gaussian Process Regression (GPR) based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure…