arXiv subjects

Tobias Buck

Publications and source records attributed to Tobias Buck.

At least 19 recordsLinked to original sources

arXiv2026

Hierarchical Bayesian inference with compositional score modeling for stellar streams

Context: Stellar streams trace the gravitational potential of the Milky Way over a wide range of Galactocentric radii. Since different streams sample different regions of the Galaxy, combining several of them can constrain the global mass distribution more tightly than modeling any single stream in isolation. Most of the existing multi-stream analyses rely on likelihood-based methods that require a new inference run whenever additional streams or kinematic measurements become available. Aims: We aim to infer the Milky Way potential from multiple stellar streams combined with an additional constraint through the Galactic circular velocity curve within a single hierarchical framework. Methods: We model the problem hierarchically, separating parameters that are common to all streams from parameters that are specific to each progenitor. We train score-based neural posterior estimators on a library of simulated streams and combine information from different streams through compositional score modeling as a post-training step. Results: Tests on independent simulations show that the inferred posteriors are well calibrated and accurate. Combining several streams reduces the uncertainties on the global potential parameters relative to single-stream analyses. Applied to Gaia data, the model favours a mildly oblate dark matter halo with axis ratio $q_{NFW} = 0.77$, scale radius $a_{NFW} = 9.3$ kpc, a disk mass of $4.3 \times 10^{10} M_\odot$, and a local dark matter density $\rho_{NFW,\odot} = 0.01153 M_\odot pc^{-3}$ consistent with recent stream-based studies. Conclusions: This hierarchical framework provides a practical way to combine information from multiple stellar streams without repeating the full inference procedure for each new dataset. The method is adaptable to new datasets, like future Gaia DR4, or new spectroscopic surveys, with minimal computational cost.

astro-ph.GA↗

arXiv2026

Per Astronomix ad Astra: High-Order Differentiable (Magneto)hydrodynamics with Energy-Conserving Self-Gravity

We present astronomix, a performant differentiable (magneto)hydrodynamics simulator written in Python/JAX. We demonstrate how automatic differentiation, validated against hand-derived analytical functional derivatives and finite differences, enables inverse modeling over millions of parameters and allows for sensitivity and stability analysis as well as correct eigenmode initialization. The differentiability of astronomix furthermore enables training machine-learning models inside the simulator. On a single GPU at a given resolution, astronomix has runtimes of the same order of magnitude as the GPU-optimized code AthenaPK but reaches far lower errors on smooth problems due to its higher order. astronomix scales to multiple GPUs ($\sim 6.5$ strong scaling speedup on $8$ GPUs) and multiple nodes ($\sim 76\%$ weak scaling efficiency on $16$ GPUs over $4$ nodes). We also present a novel fourth-order self-gravity scheme which complements the fifth-order finite difference constrained transport magnetohydrodynamics scheme implemented in astronomix. To maximize performance, we created an agentic skill that generates and validates custom Pallas GPU kernels from our JAX reference code and test suite. The simulator is available at https://github.com/leo1200/astronomix.

astro-ph.IM↗

arXiv2026

HRMOS: A High-Resolution Multi-Object Spectrograph for the VLT

This White Paper presents the scientific rationale and instrument concept for HRMOS (High-Resolution Multi-Object Spectrograph), a next-generation instrument proposed for the ESO Very Large Telescope within the VLT 2030 roadmap. Current and planned facilities offer either multi-object spectroscopy or ultra-high spectral resolution, but not both. HRMOS fills this gap by combining very high spectral resolution, multi-object capability, and radial-velocity stability, enabling transformative studies in Galactic and extragalactic astrophysics. The baseline design provides a resolving power of R = 80000, radial-velocity precision of 10 m s-1 (goal: 5 m s-1), simultaneous observations of 50-60 targets, and broad optical coverage down to 385 nm. These capabilities enable precise measurements of elemental abundances, isotopic ratios, line profiles, and radial velocities for large stellar samples, including crowded fields, star clusters, the Galactic bulge, and nearby dwarf galaxies. HRMOS will address key questions on the age of the oldest stellar populations through nucleocosmochronology, the formation and survival of planetary systems, the assembly history of the Milky Way and satellites, the origin of the heaviest elements, stellar evolution, and the chemical and dynamical properties of the interstellar and circumgalactic medium. It will bridge large spectroscopic surveys and the next generation of extremely large telescopes, with strong synergies with 4MOST, Gaia, TESS, PLATO, the proposed Haydn mission, and future ELT instruments. Building on VLT/FLAMES heritage, HRMOS represents a strategic investment for European astronomy in the 2030s.

astro-ph.IM↗

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$\alpha$, 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$\alpha$ 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

An ancient system hidden in the Galactic plane?

We analyse high signal-to-noise ESPaDOnS/CFHT spectra of 20 very metal-poor stars (VMP; [Fe/H]~$<-2.0$) in the solar neighbourhood (within $\sim2$ kpc), selected to be on planar orbits with maximum heights $\lesssim4$ kpc. The sample comprises 11 stars on prograde and 9 on retrograde orbits, all with relatively high eccentricities (0.5--0.9).Their chemical abundance patterns indicate enrichment from high-energy supernovae and hypernovae up to the Fe-peak, and contributions from fast-rotating massive stars and neutron star mergers for the neutron-capture elements. No significant chemical differences are found between prograde and retrograde stars. The [Sr, Ba, Eu/Fe] ratios resemble those of stars in classical dwarfs galaxies. Chemical dispersion and distance analyses further highlight the internal similarity of the sample and its separation from the bulk of the observed, non-planar halo population. Applying the same kinematical selection to another homogeneous dataset yields consistent results, confirming that this group of planar VMP stars exhibit peculiar chemical properties distinct from those of the observed halo and other known Galactic structures. These findings suggest that the stars formed in an environment that experienced a homogeneous chemical evolution akin to that of dwarf galaxies. A plausible scenario, supported by cosmological zoom-in simulations, is the early accretion of a single system whose subsequent dynamical evolution naturally produced stars on both prograde and retrograde planar orbits. If this interpretation is correct, we tentatively refer to this putative progenitor as \textit{Loki}. However, comparisons with other planar VMP stars spanning a wider range of chemo-dynamical properties indicate that multiple accretion events likely contributed to this diverse population orbiting close to the Galactic plane.

astro-ph.GA↗

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

The dynamical memory of tidal stellar streams: Joint inference of the Galactic potential and the progenitor of GD-1 with flow matching

Stellar streams offer one of the most sensitive probes of the Milky Way`s gravitational potential, as their phase-space morphology encodes both the tidal field of the host galaxy and the internal structure of their progenitors. In this work, we introduce a framework that leverages Flow Matching and Simulation-Based Inference (SBI) to jointly infer the parameters of the GD-1 progenitor and the global properties of the Milky Way potential. Our aim is to move beyond traditional techniques (e.g. orbit-fitting and action-angle methods) by constructing a fully Bayesian, likelihood-free posterior over both host-galaxy parameters and progenitor properties, thereby capturing the intrinsic coupling between tidal stripping dynamics and the underlying potential. To achieve this, we generate a large suite of mock GD-1-like streams using our differentiable N-body code \textsc{\texttt{Odisseo}}, sampling self-consistent initial conditions from a Plummer sphere and evolving them in a flexible Milky Way potential model. We then apply conditional Flow Matching to learn the vector field that transports a base Gaussian distribution into the posterior, enabling efficient, amortized inference directly from stream phase-space data. We demonstrate that our method successfully recovers the true parameters of a fiducial GD-1 simulation, producing well-calibrated posteriors and accurately reproducing parameter degeneracies arising from progenitor-host interactions. Flow Matching provides a powerful, flexible framework for Galactic Archaeology. Our approach enables joint inference on progenitor and Galactic parameters, capturing complex dependencies that are difficult to model with classical likelihood-based methods.

astro-ph.GA↗

arXiv2025

Solver-in-the-Loop Applications in Astrophysical (Magneto)hydrodynamics

We present two promising applications of training machine learning models inside a differentiable astrophysical (magneto)hydrodynamics simulator. First, we address the problem of slow convergence in hydrodynamical simulations of wind-blown bubbles with radiative cooling. We demonstrate that a learned cooling function can recover high-resolution dynamics in low-resolution simulations. Secondly, we train a convolutional neural network to correct 2D magnetohydrodynamics simulations of a specific blast wave problem. These case studies pave the way for the principled application of more general machine learning models inside astrophysical simulators. The code is available open source under https://github.com/leo1200/eurips25corr.

astro-ph.IM↗

arXiv2025

Differentiable N-body code for Galactic Dynamics -- Odisseo

We introduce \textsc{Odisseo} (Optimized Differentiable Integrator for Stellar Systems Evolution of Orbits), a differentiable N-body code designed to constrain the gravitational potential of the Milky Way (MW) through dynamical modeling of accreted structures such as stellar streams. \textsc{Odisseo} is implemented in JAX, enabling just-in-time compilation, automatic differentiation, and hardware acceleration on GPUs and TPUs. The code features efficient, fully vectorized force calculations and exhibits near-linear scaling when distributing a single simulation across multiple GPUs, making it suitable for large scale optimization tasks. As a demonstration, we present a case study using a mock GD-1 stellar stream simulation, where we optimize four physical parameters via gradient descent: the accretion time and progenitor mass, as well as the masses of the host Navarro-Frenk-White (NFW) halo and Miyamoto-Nagai (MN) disk. \textsc{Odisseo} accurately recovers stream morphology and underlying parameters in a differentiable and scalable framework, providing a powerful tool for dynamical studies of the Milky Way and its accreted substructures.

astro-ph.GA↗

arXiv2025

RUBIX: Differentiable forward modelling of galaxy spectral data cubes for gradient-based parameter estimation

Although integral-field spectroscopy enables spatially resolved spectral studies of galaxies, bridging particle-based simulations to observations remains slow and non-differentiable. We present RUBIX, a JAX-based pipeline that models mock integral-field unit (IFU) cubes for galaxies end-to-end and calculates gradients with respect to particle inputs. Our implementation is purely functional, sharded, and differentiable throughout. We validate the gradients against central finite differences and demonstrate gradient-based parameter estimation on controlled setups. While current experiments are limited to basic test cases, they demonstrate the feasibility of differentiable forward modelling of IFU data. This paves the way for future work scaling up to realistic galaxy cubes and enabling machine learning workflows for IFU-based inference. The source code for the RUBIX software is publicly available under https://github.com/AstroAI-Lab/rubix.

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

Lagrangian neural ODEs: Measuring the existence of a Lagrangian with Helmholtz metrics

Neural ODEs are a widely used, powerful machine learning technique in particular for physics. However, not every solution is physical in that it is an Euler-Lagrange equation. We present Helmholtz metrics to quantify this resemblance for a given ODE and demonstrate their capabilities on several fundamental systems with noise. We combine them with a second order neural ODE to form a Lagrangian neural ODE, which allows to learn Euler-Lagrange equations in a direct fashion and with zero additional inference cost. We demonstrate that, using only positional data, they can distinguish Lagrangian and non-Lagrangian systems and improve the neural ODE solutions.

cs.LG↗

arXiv2025

The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- II. Were Splash stars heated or already born hot?

One of the most debated consequences of the Milky Way's last major merger is the so-called $Splash$: stars with disc-like chemistry but halo-like kinematics, often interpreted as evidence for the violent heating of an early protodisc. Using the same high-resolution NIHAO-UHD cosmological simulation analysed in Paper I, we test whether, and if so how, a $Splash$-like population arises in the Milky Way analogue. By tracing stellar birth positions, ages, and present-day orbits, we find that protodisc stars were already born on dynamically hot orbits, with only limited additional dynamical $splashing$ of these particular in-situ stars despite a 1:5 stellar mass merger. A subset of stars, particularly those that end up in the Solar neighbourhood, shows evidence for merger-driven angular-momentum redistribution, but the overall kinematic distribution of stars with $Splash$-like chemistry remains largely unchanged. The observed $Splash$ may therefore primarily reflect the already turbulent early disc, subsequently intermixed with accreted stars and those formed from merger-driven gas inflows, rather than a distinct merger-heated population. When selecting stars with similar chemistry and age as the $Splash$-like ones, we find their azimuthal velocity distribution to be broad and positively skewed, with $V_\varphi = 73_{-59}^{+74}\,\mathrm{km\,s^{-1}}$. The transition to a rotation-supported disc with large azimuthal velocities occurs only during or after the merger. Our results suggest an alternative to the proposed $splashing$ scenario and highlight the need to disentangle the relative contributions of merger-induced heating and intrinsically hot disc formation to clarify the nature of $Splash$-like stars and their role in shaping the early Milky Way.

astro-ph.GA↗

arXiv2025

The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- I. A golden thread through time and space

Understanding how past major mergers shaped the Milky Way's present-day structure is a key goal of Galactic archaeology. The Galaxy's chemical and dynamical structure retains the imprint of such events, including a major accretion episode around 8-10 Gyr ago. Recent findings suggest that present-day orbital energy correlates with stellar chemistry and birth location within the merging progenitor galaxy. Using a high-resolution NIHAO-UHD cosmological zoom-in simulation of a Milky Way analogue, we trace the birth positions, ages, and present-day orbits of stars accreted in its last major merger. We show that stars born in the progenitor's core are more tightly bound to the Milky Way and more chemically enriched, while those from the outskirts are less bound and more metal-poor. This supports the Sk\'ulad\'ottir et al. (2025) scenario that accreted progenitor stars of different chemistry were deposited onto different orbital energies as the galaxy was stripped from the outside in, now in a cosmological context. Quantitatively, we measure a metallicity gradient with progenitor birth radius of $\mathrm{d[Fe/H]}/\mathrm{d}R_\mathrm{birth}^\prime \approx -0.05\,\mathrm{dex\,kpc^{-1}}$, demonstrating that abundance patterns retain measurable memory of formation location within the disrupted satellite. This chemodynamical memory is also evident in elemental planes such as [Al/Fe] vs. [Mg/Mn], consistent with gradients in progenitor star formation efficiency. We further show that common integrals-of-motion selections systematically miss stars from the chemically enriched core, biasing reconstructions toward the metal-poor outskirts. Together, our results demonstrate that chemodynamical memory survives the merger and can reconstruct the accreted galaxy's internal structure, while highlighting biases in current selections of accreted stars.

astro-ph.GA↗

arXiv2025

Bridging Simulations and Observations: New Insights into Galaxy Formation Simulations via Out-of-Distribution Detection and Bayesian Model Comparison

Cosmological simulations are a powerful tool to advance our understanding of galaxy formation and many simulations model key properties of real galaxies. A question that naturally arises for such simulations in light of high-quality observational data is: How close are the models to reality? Due to the high-dimensionality of the problem, many previous studies evaluate galaxy simulations using simplified summary statistics of physical properties. In this work, we combine simulation-based Bayesian model comparison with a novel misspecification detection technique to compare simulated galaxy images of 6 hydrodynamical models observations. Since cosmological simulations are computationally costly, we address the problem of low simulation budgets by first training a $k$-sparse variational autoencoder (VAE) on the abundant dataset of SDSS images. The VAE learns to extract informative latent embeddings and delineates the typical set of real images. To reveal simulation gaps, we then perform out-of-distribution detection (OOD) based on the logits of classifiers trained on the embeddings of simulated images. Finally, we perform amortized Bayesian model comparison using probabilistic classification, identifying the relatively best-performing model along with partial explanations through SHAP values.

astro-ph.GA↗

arXiv2025

A COMPASS to Model Comparison and Simulation-Based Inference in Galactic Chemical Evolution

We present COMPASS, a novel simulation-based inference framework that combines score-based diffusion models with transformer architectures to jointly perform parameter estimation and Bayesian model comparison across competing Galactic Chemical Evolution (GCE) models. COMPASS handles high-dimensional, incomplete, and variable-size stellar abundance datasets. Applied to high-precision elemental abundance measurements, COMPASS evaluates 40 combinations of nucleosynthetic yield tables. The model strongly favours Asymptotic Giant Branch yields from NuGrid and core-collapse SN yields used in the IllustrisTNG simulation, achieving near-unity cumulative posterior probability. Using the preferred model, we infer a steep high-mass IMF slope and an elevated Supernova Ia normalization, consistent with prior solar neighbourhood studies but now derived from fully amortized Bayesian inference. Our results demonstrate that modern SBI methods can robustly constrain uncertain physics in astrophysical simulators and enable principled model selection when analysing complex, simulation-based data.

astro-ph.GA↗

arXiv2025

Causal Discovery of Latent Variables in Galactic Archaeology

Galactic archaeology--the study of stellar migration histories--provides insights into galaxy formation and evolution. However, establishing causal relationships between observable stellar properties and their birth conditions remains challenging, as key properties like birth radius are not directly observable. We employ Rank-based Latent Causal Discovery (RLCD) to uncover the causal structure governing the chemodynamics of a simulated Milky Way galaxy. Using only five observable properties (metallicity, age, and orbital parameters), we recover in a purely data-driven manner a causal graph containing two latent nodes that correspond to real physical properties: the birth radius and guiding radius of stars. Our study demonstrates the potential of causal discovery models in astrophysics.

astro-ph.GA↗