Speaker
Description
The next generation radio interferometry experiments will be sensible enough not only to detect the 21-cm signal but they will be able to map the distribution of neutral hydrogen during reionization and produce a tremendous amount of 3D tomographic data. The biggest challenge for the observational analysis of these images is to separate the 21-cm signal from the undesired foreground and instrumental noise contaminations.
Here, we present SERENEt (SEgmentation and REcovery NEtwork). A deep learning approach that works on SKA-Low mock observation with an observation time of 1000 h and in the presence of the Galactic synchrotron foreground. Our network identifies regions of neutral hydrogen (HI) and recovers the reionization 21-cm signal from those regions identified as neutral. We show that our approach can identify neutral regions during reionization with more than 87 percent accuracy and recover the 21-cm 2D power spectra with an average of 95 percent accuracy.
