arXiv subjects

Rami Al-Belmpeisi

Publications and source records attributed to Rami Al-Belmpeisi.

3 recordsLinked to original sources

arXiv2026

A deep learning algorithm for black hole spin estimation using hot-spot secondary images

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 $\sim100.000$ 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 ($σ_{a_*}=0.04,\,σ_i=2^{\circ}$), 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.

astro-ph.HE↗

arXiv2023

Simulated Analogues I: apparent and physical evolution of young binary protostellar systems

Protostellar binaries harbour complex environment morphologies. Observations represent a snapshot in time, and projection and optical depth effects impair our ability to interpret them. Careful comparison with high-resolution models that include the larger star-forming region can help isolate the driving physical processes and give observations context in the time domain. We carry out zoom-in simulations with AU-scale resolution, and for the first time ever we follow the evolution until a circumbinary disk is formed. We investigate the gas dynamics around the young stars and extract disk sizes. Using radiative transfer, we obtain evolutionary tracers of the binary systems. We find that the centrifugal radius in prestellar cores is a poor estimator of the resulting disk size due to angular momentum transport at all scales. For binaries, the disk sizes are regulated periodically by the binary orbit, having larger radii close to the apastron. The bolometric temperature differs systematically between edge-on and face-on views and shows a high frequency time dependence correlated with the binary orbit and a low frequency time dependence with larger episodic accretion events. These oscillations can bring the system appearance to change rapidly from class 0 to class I and for short time periods even bring it to class II. The highly complex structure in early stages, as well as the binary orbit itself, affects the classical interpretation of protostellar classes and direct translation to evolutionary stages has to be done with caution and include other evolutionary indicators such as the extent of envelope material.

astro-ph.SR↗

arXiv2023

Simulated analogues II: a new methodology for non-parametric matching of models to observations

Star formation is a multi-scale problem, and only global simulations that account for the connection from the molecular cloud scale gas flow to the accreting protostar can reflect the observed complexity of protostellar systems. Star-forming regions are characterised by supersonic turbulence and as a result, it is not possible to simultaneously design models that account for the larger environment and in detail reproduce observed stellar systems. Instead, the stellar inventories can be matched statistically, and best matches found that approximate specific observations. Observationally, a combination of single-dish telescopes and interferometers are now able to resolve the nearest protostellar objects on all scales from the protostellar core to the inner 10 AU. We present a new non-parametric methodology which uses high-resolution simulations and post-processing methods to match simulations and observations using deep learning. Our goal is to perform a down-selection from large data sets of synthetic images to a ranked list of best-matching candidates with respect to the observation. This is particularly useful for binary and multiple stellar systems that form in turbulent environments. The objective is to accelerate the rate at which we can do such comparisons, remove biases from hand-picking matches, and contribute to identifying the underlying physical processes that drive the creation and evolution of observed protostellar systems.

astro-ph.GA↗