Sub-parsec resolution cosmological simulations of star-forming clumps at high redshift with feedback of individual stars
F. Calura (1), A. Lupi (2,3), J. Rosdahl (4), E. Vanzella (1), M., Meneghetti (1), P. Rosati (5), E. Vesperini (6), E. Lacchin (1,7), R. Pascale, (1), R. Gilli (1) ((1) INAF-Osservatorio di Astrofisica e Scienza dello, Spazio, Bologna, Italy, (2) Universita' di Milano-Bicocca

TL;DR
This paper presents high-resolution cosmological simulations of star-forming clumps at high redshift, incorporating detailed feedback from individual stars, to better understand the properties and formation of these early universe structures.
Contribution
The study introduces a novel simulation approach with sub-parsec resolution and stochastic stellar feedback, accurately modeling faint, magnified star-forming clumps at z=6.14.
Findings
Simulated stellar mass at z=6.14 matches observations.
Clump sizes are larger and densities lower than observed, indicating model limitations.
Clumps occupy an intermediate size-mass sequence between high-redshift and local dwarf galaxies.
Abstract
We introduce a new set of zoom-in cosmological simulations with sub-pc resolution, intended to model extremely faint, highly magnified star-forming stellar clumps, detected at z=6.14 thanks to gravitational lensing. The simulations include feedback from individual massive stars (in both the pre-supernova and supernova phases), generated via stochastic, direct sampling of the stellar initial mass function. We adopt a modified 'delayed cooling' feedback scheme, specifically created to prevent artificial radiative loss of the energy injected by individual stars in very dense gas (n~10^3-10^5 cm^{-3}). The sites where star formation ignites are characterised by maximum densities of the order of 10^5 cm^{-3} and gravitational pressures P/k>10^7 K/cm^3, corresponding to the values of the local, turbulent regions where the densest stellar aggregates form. The total stellar mass at z=6.14 is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
