A deep learning algorithm for black hole spin estimation using hot-spot secondary images
Abstract
Sagittarius A* exhibits frequent flaring activity across the electromagnetic spectrum that is often associated with a localized region of strong emission known as a hot spot. We aim to train a deep learning model to provide a link between key parameters of this phenomenon - hot-spot emission radius, and black hole inclination and spin - to the observed angle difference between the primary and secondary image (ΔPA) that present and future interferometric arrays could resolve. Using the general relativistic radiative transfer code IPOLE, we generated a library of ∼100.000 models with varying system parameters and computed the position angle difference on the image plane between the primary and secondary images of the hot spot. We explore equatorial and non-equatorial circular orbits and evaluate our models against approximate observational constraints, including partial-orbit visibility and observational errors. Our algorithm STIHOS shows remarkable accuracy in calculating spin and inclination from the majority of the observational tests we perform (σa*=0.04, σi=2∘), even in extreme conditions where only half of the orbit is visible. The off-equatorial estimation provides softer constraints in the absence of prior information. Our results demonstrate the importance of hot-spot observations for spacetime estimations. Given the increasing efforts to detect the photon-ring, our framework could prove valuable in interpreting the first observations of lensed emission.
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BibTeX
@misc{yfantis2026deep,
title = {{A deep learning algorithm for black hole spin estimation using hot-spot secondary images}},
author = {Aristomenis I. Yfantis and Rami Al-Belmpeisi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.18208},
eprint = {2608.18208},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.18208},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A deep learning algorithm for black hole spin estimation using hot-spot secondary images
AU - Aristomenis I. Yfantis
AU - Rami Al-Belmpeisi
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2608.18208
UR - https://arxiv.org/abs/2608.18208
AB - Sagittarius A* exhibits frequent flaring activity across the electromagnetic spectrum that is often associated with a localized region of strong emission known as a hot spot. We aim to train a deep learning model to provide a link between key parameters of this phenomenon - hot-spot emission radius, and black hole inclination and spin - to the observed angle difference between the primary and secondary image (ΔPA) that present and future interferometric arrays could resolve. Using the general relativistic radiative transfer code IPOLE, we generated a library of ∼100.000 models with varying system parameters and computed the position angle difference on the image plane between the primary and secondary images of the hot spot. We explore equatorial and non-equatorial circular orbits and evaluate our models against approximate observational constraints, including partial-orbit visibility and observational errors. Our algorithm STIHOS shows remarkable accuracy in calculating spin and inclination from the majority of the observational tests we perform (σ_a_*=0.04, σ_i=2^∘), even in extreme conditions where only half of the orbit is visible. The off-equatorial estimation provides softer constraints in the absence of prior information. Our results demonstrate the importance of hot-spot observations for spacetime estimations. Given the increasing efforts to detect the photon-ring, our framework could prove valuable in interpreting the first observations of lensed emission.
ER -