arXiv subjects

Lorenzo Branca

Publications and source records attributed to Lorenzo Branca.

9 recordsLinked to original sources

arXiv2026

Amortized Simulation-Based Inference of Colliding-Wind Binaries from Short, Noisy Image Time Series

Colliding-wind binaries (CWBs), which are systems of two massive stars whose supersonic winds collide into bow shocks, encode rich information about stellar wind properties in their multi-frequency emission, e.g. images in the H$α$, X-ray, and radio wavelengths. Inferring physical parameters (mass-loss rates, terminal wind velocities, orbital elements) from short time-series observations is a compelling but challenging inverse problem, because the forward hydrodynamic simulator is computationally expensive and the likelihood is intractable. We adopt a factorized spatio-temporal architecture for amortized posterior inference that separates spatial encoding from temporal aggregation. This design aligns with the structure of the underlying physical process of local morphology and global dynamical evolution, induces time-translation equivariance in the learned representation, and improves identifiability in low-signal regimes. Coupled with a neural spline flow conditioned on these spatio-temporal embeddings of 10-frame H$α$ photon-count time series, we present a complete simulation-based inference pipeline for CWBs. Our method jointly infers seven physical parameters from synthetic observations under realistic detector noise, with posteriors verified as well-calibrated via TARP and SBC diagnostics. The approach naturally expands posterior width in information-poor regimes (low photon counts) and robustly recovers orbital parameters and mass-loss rates, demonstrating the feasibility of amortized likelihood-free inference for this challenging astrophysical inverse problem.

astro-ph.SR↗

arXiv2026

Systematic selection of surrogate models for nonequilibrium chemistry

Nonequilibrium chemistry is central to many astrophysical environments but remains a major computational bottleneck in simulations because solving the associated stiff ODE systems is expensive. Neural surrogates promise large speedups, yet existing studies rarely provide systematic comparisons of architectures or rigorous optimization toward both accuracy and efficiency. We introduce CODES, a principled framework for optimizing and benchmarking astrochemical surrogate models. Using CODES, we compare four neural surrogate architectures across four KROME-generated datasets spanning primordial and molecular-cloud chemistry with up to 287 reactions across 37 species. Dual-objective optimization reveals pronounced accuracy-efficiency trade-offs across architectures. Fully connected models achieve the highest accuracy and most reliable uncertainty estimates, while latent-evolution models show improved robustness under iterative prediction. Our results highlight the importance of systematic optimization and architectural comparison. The datasets, metrics, and benchmarking procedure are publicly released within CODES to enable reproducible surrogate benchmarking.

astro-ph.GA↗

arXiv2025

Carbox: an end-to-end differentiable astrochemical simulation framework

Since the first observations of interstellar molecules, astrochemical simulations have been employed to model and understand its formation and destruction path- ways. With the advent of high-resolution telescopes such as JWST and ALMA, the number of detected molecules has increased significantly, thereby creating a need for increasingly complex chemical reaction networks. To model such complex systems, we have developed Carbox, a new astrochemical simulation code that leverages the modern high-performance transformation framework Jax. With Jax enabling computational efficiency and differentiability, Carbox can easily utilize GPU acceleration, be used to study sensitivity and uncertainty, and interface with advances in Scientific Machine Learning. All of these features are crucial for modeling the molecules observed by current and next-generation telescopes.

astro-ph.GA↗

arXiv2025

Ray-trax: Fast, Time-Dependent, and Differentiable Ray Tracing for On-the-fly Radiative Transfer in Turbulent Astrophysical Flows

Radiative transfer is a key bottleneck in computational astrophysics: it is nonlocal, stiff, and tightly coupled to hydrodynamics. We introduce Ray-trax, a GPU-oriented, fully differentiable 3D ray tracer written in JAX that solves the time-dependent emission--absorption problem and runs directly on turbulent gas fields produced by hydrodynamic simulations. The method favors the widely used on-the-fly emission--absorption approximation, which is state of the art in many production hydro codes when scattering is isotropic. Ray-trax vectorizes across rays and sources, supports arbitrarily many frequency bins without architectural changes, and exposes end-to-end gradients, making it straightforward to couple with differentiable hydro solvers while preserving differentiability. We validate against analytical solutions, demonstrate propagation in turbulent media, and perform a simple inverse problem via gradient-based optimization. In practice, the memory footprint scales as $\mathcal{O}(N_{\text{src}}\,N_{\text{cells}})$ while remaining highly efficient on accelerators.

astro-ph.IM↗

arXiv2025

Emulating Radiative Transfer in Astrophysical Environments

Radiative transfer is a fundamental process in astrophysics, essential for both interpreting observations and modeling thermal and dynamical feedback in simulations via ionizing radiation and photon pressure. However, numerically solving the underlying radiative transfer equation is computationally intensive due to the complex interaction of light with matter and the disparity between the speed of light and the typical gas velocities in astrophysical environments, making it particularly expensive to include the effects of on-the-fly radiation in hydrodynamic simulations. This motivates the development of surrogate models that can significantly accelerate radiative transfer calculations while preserving high accuracy. We present a surrogate model based on a Fourier Neural Operator architecture combined with U-Nets. Our model approximates three-dimensional, monochromatic radiative transfer in time-dependent regimes, in absorption-emission approximation, achieving speedups of more than 2 orders of magnitude while maintaining an average relative error below 3%, demonstrating our approach's potential to be integrated into state-of-the-art hydrodynamic simulations.

astro-ph.IM↗

arXiv2025

Stellar halos tracing the assembly of ultra-faint dwarf galaxies

[Abridged]Ultra-faint dwarfs (UFDs) are expected to be the relics of the earliest galaxies forming in the Universe. Observations show the presence of a stellar halo around them, which can give precious insights into the evolution of UFDs. This work investigates how merger properties impact the formation of stellar halos around UFDs, focusing on Tucana II, the most promising UFD assembled through mergers. We develop N-body simulations of isolated mergers between two UFDs with $1M_\odot$ stellar resolution. We build a suite of simulations by varying: i) the merger mass ratio, $M_1/M_2$, the specific ii) kinetic energy, $k$, and iii) angular momentum, $l$, iv) the dark-to-stellar mass ratio, $M_{DM}/M_*$, of the progenitors and iv) their stellar size, $R_{1/2}$. We use a neural network to explore the parameter space, emulating the properties of the "post-merger" UFD by quantifying the half-mass radius ($R_*$) and the fraction of stars at radii $>5R_*$ ($f_5$). Our principal component analysis clearly shows that $f_5$ ($R_*$) is primarily determined by $M_1/M_2$ ($R_{1/2}$), with $R_{1/2}$ ($M_1/M_2$) playing a secondary role. Both $f_5$ and $R_*$ show almost no dependence on $k$, $l$, and $M_{DM}/M_*$ in the explored range. Using our emulator, we find that to form the stellar halo observed in Tucana II, i.e. $f_5=10\pm5\%$ and $R_*=120\pm30$pc, we need $M_1/M_2=8_{-3}^{+4}$ and $R_{1/2}=97^{+25}_{-18}$pc. Such findings are corroborated by the consistency ($χ^2=0.5-2$) between the stellar density profile observed and those of simulations having $M_1/M_2$ and $R_{1/2}$ close to the emulator's predictions. Ongoing and planned spectroscopic surveys will greatly increase the statistics of observed stars and thus stellar halos in UFDs. By interpreting such observations with our model, we will provide new insights into the assembly history of UFDs and thus on the early galaxy formation process.

astro-ph.GA↗

arXiv2024

Emulating the interstellar medium chemistry with neural operators

Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM). Computationally, solving chemistry is among the most heavy tasks in cosmological and astrophysical simulations. The evolution of such non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim at substituting such procedural solvers with fast, pre-trained, emulators based on neural operators. We emulate a non-equilibrium chemical network up to H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, i.e. by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks. We use $\texttt{KROME}$ to generate a training set spanning $-2\leq \log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$, $-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field $\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared with the reference solutions obtained by $\texttt{KROME}$ for single zone models, the typical precision obtained is of order $10^{-2}$, i.e. the $10 \times$ better with a training that is $40 \times$ less costly with respect to previous emulators which however considered only a fixed $\mathbf{F}$. The present model achieves a speed-up of a factor of $128 \times$ with respect to stiff ODE solvers. Our neural emulator represents a significant leap forward in the modeling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency.

astro-ph.GA↗

arXiv2022

Neural networks: solving the chemistry of the interstellar medium

Non-equilibrium chemistry is a key process in the study of the InterStellar Medium (ISM), in particular the formation of molecular clouds and thus stars. However, computationally it is among the most difficult tasks to include in astrophysical simulations, because of the typically high (>40) number of reactions, the short evolutionary timescales (about $10^4$ times less than the ISM dynamical time) and the characteristic non-linearity and stiffness of the associated Ordinary Differential Equations system (ODEs). In this proof of concept work, we show that Physics Informed Neural Networks (PINN) are a viable alternative to traditional ODE time integrators for stiff thermo-chemical systems, i.e. up to molecular hydrogen formation (9 species and 46 reactions). Testing different chemical networks in a wide range of densities ($-2< \log n/{\rm cm}^{-3}< 3$) and temperatures ($1 < \log T/{\rm K}< 5$), we find that a basic architecture can give a comfortable convergence only for simplified chemical systems: to properly capture the sudden chemical and thermal variations a Deep Galerkin Method is needed. Once trained ($\sim 10^3$ GPUhr), the PINN well reproduces the strong non-linear nature of the solutions (errors $\lesssim 10\%$) and can give speed-ups up to a factor of $\sim 200$ with respect to traditional ODE solvers. Further, the latter have completion times that vary by about $\sim 30\%$ for different initial $n$ and $T$, while the PINN method gives negligible variations. Both the speed-up and the potential improvement in load balancing imply that PINN-powered simulations are a very palatable way to solve complex chemical calculation in astrophysical and cosmological problems.

astro-ph.GA↗

arXiv2020

Thermodynamics conditions of matter in the neutrino decoupling region during neutron star mergers

Neutrino-matter interactions play a key role in binary neutron star mergers. Thermodynamics conditions at the surfaces where neutrinos decouple from matter influence neutrino spectra, ultimately affecting the evolution of the remnant and the properties of the ejecta. In this work, we post-process results of general relativistic merger simulations employing microphysical equations of state and approximate neutrino transport to investigate the thermodynamics conditions at which weak and thermal equilibrium freezes out (equilibrium surfaces), as well as conditions at which the transition between diffusion and free-streaming regime occurs (diffusion surfaces). We find that the rest mass density and the neutrino energy are the most relevant quantities in determining the location of the decoupling surfaces. For mean energy neutrinos ($\langle E_{ν_e} \rangle \approx 9~{\rm MeV}$, $\langle E_{{\barν}_e} \rangle \approx 15~{\rm MeV}$, $\langle E_{ν_{μ,τ}} \rangle \approx 25~{\rm MeV}$), diffusion surfaces are located around $10^{11}{\rm g~cm^{-3}}$ for all neutrino species, while equilibrium surfaces for heavy flavor neutrinos are significantly deeper (several $10^{12}{\rm g~cm^{-3}}$) than the ones of $\barν_e$ and $ν_e$ ($\gtrsim 10^{11}{\rm g~cm^{-3}}$). The resulting decoupling temperatures are in good agreement with the average neutrino energies ($\langle E_ν \rangle \sim 3.15~T$), with the softer equation of state characterized by systematically larger decoupling temperatures ($ΔT \lesssim 1~{\rm MeV}$). Neutrinos streaming at infinity with different energies come from very different regions of the remnant. The presence of a massive NS or of a BH in the remnant influences the neutrino thermalization process.

astro-ph.HE↗