Identification of Candidate Halos Hosting Massive Black Hole Seeds in the $\textit{Renaissance}$ Simulations with Support Vector Machines

TL;DR

This paper uses support vector machines to identify early universe halos likely to host direct collapse black holes, improving predictions of supermassive black hole origins.

Contribution

It introduces a machine learning approach to predict DCBH-hosting halos based on properties like metallicity and radiation flux, enhancing simulation modeling.

Findings

Best SVM model uses star formation-related features.

Model achieves high accuracy in classifying potential DCBH hosts.

Provides probabilistic seeding prescriptions for cosmological simulations.

Abstract

The nature of the origins of supermassive black holes remains uncertain. Multiple possible seeding pathways have been proposed across a variety of mass scales, each with their own strengths and weaknesses. One such channel is a direct collapse black hole (DCBH), thought to form from the deaths of supermassive stars in pristine atomic cooling halos in the early universe. In this work, we investigate the ability to identify halos likely to form a DCBH based on their properties using a support vector machine (SVM). We implement multiple methods to improve the accuracy of the model, including selecting subsets of critical features and optimizing SVM hyperparameters. We find that our best model requires quantities relevant to star formation, such as the metallicity, incident flux of Lyman-Werner radiation, and halo stellar mass. The SVMs produced from this work can serve as probabilistic and holistic seeding prescriptions for DCBHs in cosmological simulations.