arXiv subjects

Pedro Naethe Motta

Publications and source records attributed to Pedro Naethe Motta.

4 recordsLinked to original sources

arXiv2026

Sensitivities of Black Hole Images from GRMHD Simulations

The advent of high-fidelity imaging of supermassive black holes calls for efficient and robust data-analysis methods. In this work, we use $\texttt{Jipole}$, a differentiable, $\texttt{ipole}$-based radiative transfer code, to enable gradient-based analyses of images generated from state-of-the-art general relativistic magnetohydrodynamic (GRMHD) simulations. We compute image sensitivities, i.e., pixel-wise derivatives of the intensity with respect to model parameters, which form the Jacobian of the forward model and define a local map from parameter space to image space. Using these sensitivities in a mock data analysis, we find that GRMHD-based images generate a structured error landscape for parameter fitting, with anisotropies and local minima, making parameter exploration nontrivial but still tractable when guided by gradient information. We characterize this landscape through the Jacobian and assess the feasibility of gradient-based recovery under idealized, blurred, and noisy conditions. Our results show that automatic differentiation-computed image gradients can guide parameter exploration effectively even in the presence of noise. These findings establish a basis for efficient, high-precision model--data comparisons in black hole imaging and motivate the integration of these sensitivities into advanced inference frameworks.

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arXiv2026

$\texttt{GPUmonty}$: A GPU-accelerated relativistic Monte Carlo radiative transfer code

We introduce $\texttt{GPUmonty}$, a CUDA/C-based Monte Carlo radiative transfer code accelerated using graphics processing units (GPUs). $\texttt{GPUmonty}$ derives from the CPU-based code $\texttt{grmonty}$ and offloads the most computationally expensive stages of the calculation -- superphoton generation, sampling, tracking, and scattering -- to the GPU. Whereas $\texttt{grmonty}$ handles photons sequentially, $\texttt{GPUmonty}$ processes large numbers of superphotons concurrently, leveraging the single-instruction, multiple-thread (SIMT) execution model of modern GPUs. Benchmarks demonstrate a speedup of about $12\times$ relative to the original CPU implementation on a single GPU, with runtime limited primarily by register pressure rather than compute or memory bandwidth saturation. We validate the implementation through analytic tests for a optically thin synchrotron sphere, as well as comparisons with $\texttt{igrmonty}$ for scattering synchrotron sphere and GRMHD simulation data. Relative errors remain below a percent level and convergence is consistent with the expected $N_{\rm s}^{-1/2}$ Monte Carlo scaling. By significantly reducing computational costs, GPUmonty enables the extensive parameter space surveys and faster spectra modeling required to interpret horizon-scale observations of supermassive black holes. $\texttt{GPUmonty}$ is publicly available under the GNU General Public License.

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arXiv2026

$\texttt{Jipole}$: A Differentiable $\texttt{ipole}$-based Code for Radiative Transfer in Curved Spacetimes

Recent imaging of supermassive black holes by the Event Horizon Telescope (EHT) has relied on exhaustive parameter-space searches, matching observations to large, precomputed libraries of theoretical models. As observational data become increasingly precise, the limitations of this computationally expensive approach grow more acute, creating a pressing need for more efficient methods. In this work, we present $\texttt{Jipole}$, an automatically differentiable (AD), $\texttt{ipole}$-based code for radiative transfer in curved spacetimes, designed to compute image gradients with respect to underlying model parameters. These gradients quantify how parameter changes-such as the black hole's spin or the observer's inclination-affect the image, enabling more efficient parameter estimation and reducing the number of required images. We validate $\texttt{Jipole}$ against $\texttt{ipole}$ in two analytical tests and then compare pixel-wise intensity derivatives from AD with those from finite-difference methods. We then demonstrate the utility of these gradients by performing parameter recovery for an analytical model using a conjugate gradient optimizer in three increasingly complex cases for the injected image: ideal, blurred, and blurred with added noise. In most cases, high-accuracy fits are obtained in only a few optimization steps, failing only in cases with extremely low signal-to-noise ratios and large blurring size kernels. These results highlight the potential of AD-based methods to accelerate robust, high-fidelity model-data comparisons in current and future black hole imaging efforts.

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arXiv2025

Black hole spectral states revealed in GRMHD simulations with texture memory accelerated cooling

X-ray binaries (XRBs) display spectral state transitions that are accompanied by substantial changes in the hardness, luminosity, and structure of the accretion flow. We developed a GPU-accelerated cooling toolkit for general relativistic magnetohydrodynamic (GRMHD) simulations of accreting black holes that uses texture memory for fast retrieval of pre-computed values. The toolkit incorporates bremsstrahlung, synchrotron, inverse Compton radiation and Coulomb collision processes. We implemented our toolkit into a GRMHD code and used it to simulate a magnetically arrested disk in the context of the XRB low/hard state around a Kerr black hole. We explored the mass accretion rate in the $\sim (10^{-6}-0.3) \dot{M}_{\rm Edd}$ range, where $\dot{M}_{\rm Edd}$ is the Eddington accretion rate. Our simulations reveal that for low accretion rates ($\dot{M} \lesssim 0.01 \dot{M}_{\rm Edd}$), the flow settles into a geometrically thick, low-density, two-temperature hot accretion flow. At higher accretion rates, the flow turns into a cold single-temperature thin disk at $r_{\rm in} \gtrsim 50 r_g$. Inside, the disk breaks up into single-temperature thin filaments embedded into a two-temperature hot thick flow. Our GPU texture memory accelerated cooling prescription is $3-5$ times faster than the standard radiation M1 closure methods, and $\sim5$ times faster than storing the lookup table in global memory.

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