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Inferring Galactic Parameters from Chemical Abundances with Simulation-Based Inference

T Buck, B Günes, G Viterbo, WH Oliver… - arXiv preprint arXiv …, 2025 - arxiv.org
Mathematics paper astro-ph.GA Suggest

… Our study demonstrates that simulation-based inference (SBI) provides a powerful and efficient alternative to conventional methods such as Hamiltonian Monte Carlo (…

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BibTeX

@article{2503.02456v1,
Author        = {Tobias Buck and Berkay Günes and Giuseppe Viterbo and William H. Oliver and Sven Buder},
Title         = {Inferring Galactic Parameters from Chemical Abundances with
  Simulation-Based Inference},
Eprint        = {2503.02456v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.GA},
Abstract      = {Galactic chemical abundances provide crucial insights into fundamental
galactic parameters, such as the high-mass slope of the initial mass function
(IMF) and the normalization of Type Ia supernova (SN Ia) rates. Constraining
these parameters is essential for advancing our understanding of stellar
feedback, metal enrichment, and galaxy formation processes. However,
traditional Bayesian inference techniques, such as Hamiltonian Monte Carlo
(HMC), are computationally prohibitive when applied to large datasets of modern
stellar surveys. We leverage simulation-based-inference (SBI) as a scalable,
robust, and efficient method for constraining galactic parameters from stellar
chemical abundances and demonstrate its the advantages over HMC in terms of
speed, scalability, and robustness against model misspecifications. We combine
a Galactic Chemical Evolution (GCE) model, CHEMPY, with a neural network
emulator and a Neural Posterior Estimator (NPE) to train our SBI pipeline. Mock
datasets are generated using CHEMPY, including scenarios with mismatched
nucleosynthetic yields, with additional tests conducted on data from a
simulated Milky Way-like galaxy. SBI results are benchmarked against HMC-based
inference, focusing on computational performance, accuracy, and resilience to
systematic discrepancies. SBI achieves a $\sim75,600\times$ speed-up compared
to HMC, reducing inference runtime from $\gtrsim42$ hours to mere seconds for
thousands of stars. Inference on $1,000$ stars yields precise estimates for the
IMF slope ($\alpha_{\rm IMF} = -2.298 \pm 0.002$) and SN Ia normalization
($\log_{10}(N_{\rm Ia}) = -2.885 \pm 0.003$), deviating less than 0.05% from
the ground truth. SBI also demonstrates similar robustness to model
misspecification than HMC, recovering accurate parameters even with alternate
yield tables or data from a cosmological simulation. (shortened...)},
Year          = {2025},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2503.02456v1},
File          = {2503.02456v1.pdf}
}

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