Assessing the capability of random forest to predict the evolution of enhanced gamma-ray states of active galactic nuclei

TL;DR

This study evaluates the use of machine learning, specifically random forests, to predict the evolution of gamma-ray flux states in active galactic nuclei, aiding rapid follow-up observations with high accuracy.

Contribution

It introduces a machine learning approach to predict gamma-ray flux evolution in AGN, demonstrating partial flux prediction and trend forecasting capabilities.

Findings

Approximate 35% flux prediction accuracy over one day.

60-75% probability of correctly predicting rising or falling emission trends.

Predictions are influenced more by source properties than recent measurements.

Abstract

Large fraction of studies of active galactic nuclei objects is based on performing follow-up observations using high-sensitivity instruments of high flux states observed by monitoring instruments (the so-called Target of Opportunity, ToO). Due to transient nature of such enhanced states it is essential to quickly evaluate if such a ToO event should be followed. We use a machine learning method to assess the possibility to predict the evolution of high flux states in gamma-ray band observed with Fermi-LAT in context of following such alerts with current and future Cherenkov telescopes. We probe flux and Test Statistic predictions using different training schemes and sample selections. We conclude that a partial prediction of the flux over a time scale of one day with an accuracy of ~35% is possible. The method provides accurate predictions of the raising/falling emission trend with 60 - 75% probability, however deeper investigations shows that this is likely based on typical properties of the source, rather than on the result of most recent measurements.