The Two-Infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning
Niall Miller, Meridith Joyce, Christian I. Johnson, Jamie Tayar, Thomas Trueman, and R Michael Rich

TL;DR
This study uses extensive chemical evolution modeling and machine learning to constrain the formation history of the Milky Way bulge, revealing a two-phase formation with an early rapid starburst followed by a delayed infall.
Contribution
It introduces a comprehensive GCE model combined with machine learning to analyze bulge formation, providing new constraints on infall timing, star formation efficiency, and mass partitioning.
Findings
Bulge formed through an early rapid starburst and a delayed infall.
Model reproduces the metallicity distribution and alpha-element trends.
Supports a composite bulge origin with classical and younger components.
Abstract
We constrain the formation history of the Milky Way bulge using a two-infall Galactic Chemical Evolution (GCE) framework implemented in the OMEGA++ code. We recover a best-fit scenario in which the bulge forms through an early, rapid starburst (t1 ~ 0.1Gyr, tau1 ~ 0.09Gyr, star-formation efficiency (SFE) ~ 3Gyr^-1 followed by a delayed, lower mass second infall (t2 ~ 5.1Gyr, tau2 ~ 1.7Gyr, sigma2 ~ 0.69). Our model adopts mass- and metallicity-dependent nucleosynthetic yields from modern stellar grids and explores a wide GCE parameter space in infall timing, star formation efficiency, mass partitioning, IMF upper mass, and SN Ia normalization, optimized via a hybrid genetic algorithm with MCMC refinement. The later infall features a reduced star formation efficiency (Delta SFE ~ 0.72), reproducing the metal-rich peak of the bulge metallicity distribution function (MDF) and the decline…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Gamma-ray bursts and supernovae · Astronomy and Astrophysical Research
