Modeling the Central Supermassive Black Hole Mass of Quasars via the LSTM Approach

Abstract

Abstract One of the fundamental questions about quasars is related to their central supermassive black holes. The reason for the existence of these black holes with such a huge mass is still unclear, and various models have been proposed to explain them. However, there is still no comprehensive explanation that is accepted by the community. The only thing we are sure of is that these black holes were not created by the collapse of giant stars or the accretion of matter around them. Moreover, another important question is related to the mass distribution of these black holes over time. Observations have shown that if we go back through redshift, we see black holes with more mass, and after passing the peak of star formation redshift, this procedure decreases. Nevertheless, the exact redshift of this peak is still controversial. In this paper, with the help of deep learning and the LSTM algorithm, we try to find a suitable model for the mass of the central black holes of quasars over time by considering both the QUOTAS and QuasarNET data sets. Our model was built with these data reported from redshift 3 to 7 and for two redshift intervals, 0–3 and 7–10, and it predicted the mass of the quasars’ central supermassive black holes. We have also tested our model for the specified intervals with observed data from central black holes and discussed the results.