http://arxiv.org/abs/2107.04571
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.
T. Fidor and J. Sitarek
Mon, 12 Jul 21
32/49
Comments: 12 pages, 10 figures, 4 tables, accepted for publication in Astroparticle Physics
You must be logged in to post a comment.