Jason Ran | Searcharxiv

arXiv2026

sponchpop: Population synthesis to investigate volatile sulfur as a fingerprint of gas giant formation histories

Planet population synthesis is an integral tool for linking exoplanets to their formation environments. Most planet population synthesis studies have focused on the carbon-to-oxygen ratio (C/O) in gas or solids, yet more insight into planet formation may be afforded by considering a wider suite of elements. Sulfur is one such key element. It has been assumed to be entirely refractory in population synthesis models, restricting it to being a tracer of accreted rocky solids. However, sulfur also has a volatile reservoir dominant at the onset of star and planet formation, which is then converted into refractories. We investigate sulfur's wider potential as a formation history tracer by implementing a gas-grain chemical conversion, the first multi-phase treatment of S in a planet population synthesis model. We also present the planet formation module of SPONCHPOP and its first predicted planet growth tracks and populations. We apply these to explore the diversity of the planetary sulfur budget. We show that planets can inherit a wide range of core and envelope sulfur content, depending on their formation environment and accretion history including late-stage infall, demonstrating sulfur's new potential as a diagnostic tool for planet formation. Our models predict that some rocky planets are born sulfur-poor, which may have significant implications for their geochemistry and habitability. Enhanced sulfur abundances in gas-giant atmospheres, such as in our solar system, may result not only from accretion of rocky planetesimals, but also from formation beyond the H2S iceline.

astro-ph.EP↗

arXiv2025

SIMBA: A Python-based single-point astrochemical solver and analysis tool

Determining molecular abundances in astrophysical environments is crucial for interpreting observational data and constraining physical conditions in these regions. Chemical modelling tools are essential for simulating the complex processes that govern molecular evolution. We present SIMBA, a new Python-based single-point astrochemical modelling package designed to solve chemical reaction networks across diverse astrophysical environments. The software follows standardised rate equation approaches to evolve molecular abundances under specified physical conditions, incorporating gas-phase chemistry, grain-surface processes, and photochemistry. While leveraging Python for accessibility, performance-critical routines utilise just-in-time compilation to achieve computational efficiency suitable for research applications. A key feature of SIMBA is its graphical interface, which enables rapid investigation of chemical evolution under varying physical conditions. This makes it particularly valuable for exploring parameter dependencies and complementing more computationally intensive multi-dimensional models. We demonstrate the package's capabilities by modelling chemical evolution in a photoevaporative flow driven by external FUV irradiation. Using simplified gas dynamics, we chain multiple SIMBA instances to create a dynamic 1D model where gas evolves both chemically and dynamically. Comparing this approach to typical static models - where chemistry in each grid cell evolves independently - reveals that molecular ices, especially those with relatively high binding energies like H2O, can survive much farther into the flow than static models predict. This example case highlights how SIMBA can be extended to higher dimensions for investigating complex chemical processes. The package is open-source and includes comprehensive documentation.

astro-ph.IM↗