Public Release of A-SLOTH: Ancient Stars and Local Observables by Tracing Halos

Tilman Hartwig, Mattis Magg, Li Hsin Chen, Yuta Tarumi, Volker Bromm, Simon C.O. Glover, Alexander P. Ji, Ralf S. Klessen, Muhammad A. Latif, Marta Volonteri, Naoki Yoshida

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


The semianalytical model a-sloth (Ancient Stars and Local Observables by Tracing Halos) is the first public code that connects the formation of the first stars and galaxies to observables. After several successful projects with this model, we publish the source code ( and describe the public version in this paper. The model is based on dark matter merger trees that can either be generated based on Extended Press-Schechter theory or be imported from dark matter simulations. On top of these merger trees, a-sloth applies analytical recipes for baryonic physics to model the formation of both metal-free and metal-poor stars and the transition between them with unprecedented precision and fidelity. a-sloth samples individual stars and includes radiative, chemical, and mechanical feedback. It is calibrated based on six observables, such as the optical depth to Thomson scattering, the stellar mass of the Milky Way and its satellite galaxies, the number of extremely metal-poor stars, and the cosmic star formation rate density at high redshift. a-sloth has versatile applications with moderate computational requirements. It can be used to constrain the properties of the first stars and high-z galaxies based on local observables, predicts properties of the oldest and most metal-poor stars in the Milky Way, can serve as a subgrid model for larger cosmological simulations, and predicts next-generation observables of the early universe, such as supernova rates or gravitational wave events.

Original languageEnglish
Article number45
JournalAstrophysical Journal
Issue number1
Publication statusPublished - Sept 1 2022

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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