Towards a new generation of reflection models for precision measurements of accreting black holes
Cosimo Bambi
TL;DR
The paper surveys the state of X-ray reflection spectroscopy for accreting black holes, highlighting how relativistic blurring encodes strong-gravity information and current forward-modeling techniques. It identifies key modeling simplifications across disk structure, the plunging region, reflection Comptonization, emission angle, and returning radiation, noting potential biases for next-generation, high-quality data. The main contribution is a proposal to develop a new generation of reflection models using neural-network surrogates to replace bulky FITS tables and to rigorously incorporate returning radiation while remaining fast enough for routine spectral fitting. If successful, this approach will enable precise measurements of black hole spins and inclinations and robust tests of General Relativity with upcoming missions such as eXTP, Athena, and HEX-P.
Abstract
Blurred reflection features are commonly observed in the X-ray spectra of accreting black holes. In the presence of high-quality data and with the correct astrophysical model, X-ray reflection spectroscopy is a powerful tool to probe the strong gravity region of black holes, study the morphology of the accreting matter, measure black hole spins, and test Einstein's theory of General Relativity in the strong field regime. In the past 10-15 years, there has been significant progress in the development of the analysis of these reflection features, thanks to both more sophisticated theoretical models and new observational data. However, the next generation of X-ray missions (e.g. eXTP, Athena, HEX-P) promises to provide unprecedented high-quality data, which will necessarily require more accurate synthetic reflection spectra than those available today. In this talk, I will review the state-of-the-art in reflection modeling and I will present current efforts to develop a new generation of reflection models with machine learning techniques.