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

TL;DR

This paper employs an LSTM deep learning model to predict the mass evolution of supermassive black holes in quasars over cosmic time, based on QuasarNET data, addressing unresolved questions about their formation and distribution.

Contribution

It introduces a novel LSTM-based approach to model black hole mass evolution in quasars across different redshift intervals using deep learning techniques.

Findings

The model accurately predicts black hole masses from redshift 3 to 7.

Predictions align with observed data for specific redshift intervals.

The approach offers insights into black hole growth over cosmic history.

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, nor by the accretion of matter around them. Moreover, another important question is 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 masses, 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 tried to find a suitable model for the mass of central black holes of quasars over time by considering QuasarNET data. Our model was built with these data reported from redshift 3 to 7 and for two redshift intervals 0 to 3 and 7 to 10, it predicted the mass of the quasar's 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.