STARFORGE: Toward a comprehensive numerical model of star cluster formation and feedback (2010.11254v3)

Abstract: We present STARFORGE (STAR FORmation in Gaseous Environments): a new numerical framework for 3D radiation MHD simulations of star formation that simultaneously follow the formation, accretion, evolution, and dynamics of individual stars in massive giant molecular clouds (GMCs) while accounting for stellar feedback, including jets, radiative heating and momentum, stellar winds, and supernovae. We use the GIZMO code with the MFM mesh-free Lagrangian MHD method, augmented with new algorithms for gravity, timestepping, sink particle formation and accretion, stellar dynamics, and feedback coupling. We survey a wide range of numerical parameters/prescriptions for sink formation and accretion and find very small variations in star formation history and the IMF (except for intentionally-unphysical variations). Modules for mass-injecting feedback (winds, SNe, and jets) inject new gas elements on-the-fly, eliminating the lack of resolution in diffuse feedback cavities otherwise inherent in Lagrangian methods. The treatment of radiation uses GIZMO's radiative transfer solver to track 5 frequency bands (IR, optical, NUV, FUV, ionizing), coupling direct stellar emission and dust emission with gas heating and radiation pressure terms. We demonstrate accurate solutions for SNe, winds, and radiation in problems with known similarity solutions, and show that our jet module is robust to resolution and numerical details, and agrees well with previous AMR simulations. STARFORGE can scale up to massive ($>10^5 M_\odot $) GMCs on current supercomputers while predicting the stellar ($\gtrsim 0.1 M_\odot$) range of the IMF, permitting simulations of both high- and low-mass cluster formation in a wide range of conditions.

Overview of STARFORGE: Toward a Comprehensive Numerical Model of Star Cluster Formation and Feedback

The paper "STARFORGE: Toward a Comprehensive Numerical Model of Star Cluster Formation and Feedback" by Michael Y. Grudić et al. introduces a detailed framework for simulating star formation and evolution in giant molecular clouds (GMCs). The key advancement presented in this paper is the STARFORGE project, which employs the {\small GIZMO} simulation code with novel algorithms to capture the complex interactions involved in the formation of star clusters. This paper provides a comprehensive account of the numerical techniques and physics modules integrated into STARFORGE, including the simulation of stellar feedback mechanisms, radiation magnetohydrodynamics (MHD), and sink particle dynamics.

Key Aspects

1. Simulation Code and Methods: STARFORGE uses the {\small GIZMO} code, leveraging mesh-free Lagrangian MHD methods, which are modified to include modules specific to star formation processes. New algorithms cover gravity, timestepping, sink particle formation and accretion, stellar dynamics, and feedback coupling.
2. Stellar Feedback Mechanisms: The paper emphasizes capturing various feedback processes such as protostellar jets, radiative heating and momentum, stellar winds, and supernovae. These are crucial for understanding the regulation of star formation and the disruption of GMCs.
3. Framework Scalability: STARFORGE is capable of scaling simulations to GMCs as massive as $>10^5$ on current supercomputers. It predicts stellar mass functions (IMFs) across a wide range of conditions, addressing both high- and low-mass star cluster formation.
4. Physics Modules: Various non-ideal MHD physics, including Ohmic resistivity, ambipolar diffusion, and the Hall effect, are incorporated. Furthermore, dust dynamics are considered, impacting cooling, feedback coupling, and mixing.
5. Numerical Challenges Addressed: The authors detail problems in previous star formation simulations, emphasizing resolving individual star formation, modeling stellar dynamics accurately, incorporating full feedback channels, and scaling to larger GMCs.

Numerical and Physical Results

The paper reports several numerical experiments performed to test the accuracy and robustness of the STARFORGE framework:

• Sink Particle Dynamics: The robustness of sink particle formation and accretion is thoroughly tested, showing invariance across a range of parameters. This ensures that predictions depend more significantly on physical processes than numerical artifacts.
• Feedback Simulation: The team validated each feedback module against established similarity solutions or previous numerical results, demonstrating accuracy in capturing phenomena such as radiation pressure-driven shell expansions and SN remnant evolution.
• Resolution Studies: STARFORGE simulations exhibit resolution convergence for star formation efficiency (SFE) and IMF predictions, highlighting the adequacy of resolutions as coarse as $10^{-3}$ for practical applications in massive star cluster simulations.

Implications

The STARFORGE framework represents a significant advancement in simulating star formation by comprehensively incorporating realistic physics and resolving individual star dynamics without relying excessively on sub-grid prescriptions. It holds promise for:

• Understanding Star Formation: By allowing simulations to probe parameters across massive GMC scales, STARFORGE contributes to clarifying the roles of different feedback mechanisms in star formation efficiency and IMF emergence.
• Future Research Directions: Potential applications include not only localized star formation studies but also broad galactic-scale simulations where star formation impacts the larger galactic environment, with further refinements expected to overcome current limitations related to sub-grid physics and resolution constraints.
• Cross-Comparative Studies: The framework provides a basis for empirical testing of different simulation methodologies, paving the way for comparing theoretical predictions against observational data.

In summary, STARFORGE offers a remarkably detailed numerical infrastructure for star formation research, setting the stage for enhanced model accuracy through the integration of multifaceted physics effects, robust numerical methods, and scalability in simulation protocols.