2604003685
  • Open Access
  • Article

Compartmental Description of the Cosmological Baryonic Matter Cycle. Inclusion of Triggered Star Formation

  • Martin Kröger 1,2,∗,   
  • Reinhard Schlickeiser 3,4,∗

Received: 01 Apr 2026 | Revised: 24 Apr 2026 | Accepted: 27 Apr 2026 | Published: 06 May 2026

Abstract

Purpose: The earlier introduced compartmental description, well-known from the statistical description of infection diseases and epidemics, was adopted here to describe the nonlinear temporal evolution of the baryonic matter compartments in interstellar gas (G) and stars (S) in the presence of triggered star formation. The primary astrophysical goal of our study is the explanation of the cosmological star formation history. The competition of triggered star formation, spontaneous star formation, stellar feedback, and stellar evolution was theoretically investigated to understand the baryonic matter cycle, including luminous baryonic matter in main-sequence stars and weakly luminous matter in white dwarfs, neutron stars and black holes. Of particular interest was the understanding of the cosmic star formation history and the redshift dependence of the gas and stellar fractions using compartmental models. Methods: For stationary rates of spontaneous and triggered star formation, continuous stellar feedback and stellar evolution, exact and approximate analytical solutions of the time evolution of the fractions of stellar and locked-in stellar matter were derived involving the time dependence of the gaseous fraction G(t). The high accuracy of the analytical solutions is proven by comparison with the exact numerical solutions of the GSL equations. Results: The inclusion of the triggered star formation process explains the observed cosmological star formation rate, the integrated stellar density at redshifts below z = 8, and the present-day gas and stellar fractions very well. The generalized GSL-model provides excellent fits to the observed redshift dependencies of the star formation rate and the integrated stellar density. Moreover, it explains the observed present-day gas and stellar fractions, and it makes predictions on the future evolution of these fractions in the universe.

References 

  • 1.

    Schlickeiser, R.; Kroger, M. Compartmental description of the cosmological baryonic matter cycle. I. Competition of triggered star formation, stellar feedback and stellar evolution. Astron. Astrophys. 2024, 692, A64.

  • 2.

    Kermack, W.O.; McKendrick, A.G. A contribution to the mathematical theory of epidemics. Proc. R. Soc. A 1927, 115, 700–721.

  • 3.

    Kendall, D.G. Deterministic and Stochastic Epidemics in Closed Populations. In Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 26–31 December 1954; Volume 4, pp. 149–165.

  • 4.

    Estrada, E. Covid-19 and Sars-Cov-2: Modeling the present, looking at the future. Phys. Rep. 2020, 869, 1–51.

  • 5.

    Song, Z.L.; Zhang, Z.; Lyu, F.; et al. From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale. Sustainability 2024, 16, 5036.

  • 6.

    Li, G.J.; Chang, B.F.; Zhao, J.; et al. VIVIAN: Virtual simulation and visual analysis of epidemic spread data. J. Visualization 2024, 27, 677–694.

  • 7.

    Agosto, A.; Cerchiello, P. A data-driven test approach to identify Covid-19 surge phases: An alert-warning tool. Statistics 2024, 58, 422–436.

  • 8.

    Yadav, S.K.; Khan, S.A.; Tiwari, M.; et al. Taking cues from machine learning, compartmental and time series models for Sars-Cov-2 omicron infection in Indian provinces. Spat. Spatio-Temporal Epidemiol. 2024, 48, 100634.

  • 9.

    Finney, L.; Amundsen, D.E. Asymptotic analysis of periodic solutions of the seasonal SIR model. Physica D 2024, 458, 133996.

  • 10.

    Rocha, J.L.; Carvalho, S.; Coimbra, B. Probabilistic Procedures for SIR and SIS Epidemic Dynamics on Erdos-Renyi Contact Networks. Appliedmath 2023, 3, 828–850.

  • 11.

    Atienza-Diez, I.; Seoane, L.F. Long- and short-term effects of cross-immunity in epidemic dynamics. Chaos Solitons Fractals 2023, 174, 113800.

  • 12.

    Prodanov, D. Computational aspects of the approximate analytic solutions of the SIR model: Applications to modelling of Covid-19 outbreaks. Nonlinear Dyn. 2023, 111, 15613–15631.

  • 13.

    Darvishi, H.; Darvishi, M.T. An Analytical Study on Two High-Order Hybrid Methods to Solve Systems of Nonlinear Equations. J. Math. 2023, 2023, 9917774.

  • 14.

    Karaji, P.T.; Nyamoradi, N.; Ahmad, B. Stability and bifurcations of an SIR model with a nonlinear incidence rate. Math. Methods Appl. Sci. 2023, 46, 10850–10866.

  • 15.

    Chakir, Y. Global approximate solution of SIR epidemic model with constant vaccination strategy. Chaos Solitons Fractals 2023, 169, 113323.

  • 16.

    Prodanov, D. Asymptotic analysis of the SIR model and the Gompertz distribution. J. Comput. Appl. Math. 2023, 422, 114901.

  • 17.

    Gairat, A.; Shcherbakov, V. Discrete SIR model on a homogeneous tree and its continuous limit. J. Phys. A 2022, 55, 434004.

  • 18.

    de Souza, D.B.; Ara´ujo, H.A.; Duarte, G.C.; et al. Fock-space approach to stochastic susceptible-infected-recovered models. Phys. Rev. E 2022, 106, 014136.

  • 19.

    Kozyreff, G. Asymptotic solutions of the SIR and SEIR models well above the epidemic threshold. IMA J. Appl. Math. 2022, 87, 521–536.

  • 20.

    Tchoumi, S.Y.; Rwezaura, H.; Tchuenche, J.M. Dynamic of a two-strain Covid-19 model with vaccination. Results Phys. 2022, 39, 105777.

  • 21.

    Schwarzendahl, F.J.; Grauer, J.; Liebchen, B.; et al. Mutation induced infection waves in diseases like Covid-19. Sci. Rep. 2022, 12, 9641.

  • 22.

    Uc¸ar, D.; C¸ elik, E. Analysis of Covid 19 disease with SIR model and Taylor matrix method. AIMS Math. 2022, 7, 11188–11200.

  • 23.

    Hussain, S.; Madi, E.N.; Khan, H.; et al. Investigation of the Stochastic Modeling of Covid-19 with Environmental Noise from the Analytical and Numerical Point of View. Mathematics 2021, 9, 3122.

  • 24.

    Lee, K.; Parish, E.J. Parameterized neural ordinary differential equations: Applications to computational physics problems. Proc. R. Soc. A 2021, 477, 20210162.

  • 25.

    Barwolff, G. A Local and Time Resolution of the Covid-19 Propagation-A Two-Dimensional Approach for Germany Including Diffusion Phenomena. Physics 2021, 3, 536–548.

  • 26.

    Hynd, R.; Ikpe, D.; Pendleton, T. Two critical times for the SIR model. J. Math. Anal. Appl. 2022, 505, 125507.

  • 27.

    Peroux, C.; Howk, J.C. The cosmic baryon and metal cycles. Annu. Rev. Astron. Astrophys. 2020, 58, 363–406.

  • 28.

    Tortora, C.; Hunt, L.K.; Ginolfi, M. Scaling relations and baryonic cycling in local star-forming galaxies I. The sample. Astron. Astrophys. 2022, 657, A19.

  • 29.

    Mercado, F.J.; Bullock, J.S.; Boylan-Kolchin, M.; et al. A relationship between stellar metallicity gradients and galaxy age in dwarf galaxies. Mon. Not. R. Astron. Soc. 2021, 501, 5121–5134.

  • 30.

    Kroger, M.; Schlickeiser, R. Verification of the accuracy of the SIR model in forecasting based on the improved SIR model with a constant ratio of recovery to infection rate by comparing with monitored second wave data. R. Soc. Open Sci. 2021, 8, 211379.

  • 31.

    Tumlinson, J.; Peeples, M.S.; Werk, J.K. The circumgalactic medium. Annu. Rev. Astron. Astrophys. 2017, 55, 389–432.

  • 32.

    Pandya, V.; Fielding, D.B.; Bryan, G.L.; et al. A Unified Model for the Coevolution of Galaxies and Their Circumgalactic Medium: The Relative Roles of Turbulence and Atomic Cooling Physics. Astrophys. J. 2023, 956, 118.

  • 33.

    Haas, F.; Kroger, M.; Schlickeiser, R. Multi-Hamiltonian structure of the epidemics model accounting for vaccinations and a suitable test for the accuracy of its numerical solvers. J. Phys. A 2022, 55, 225206.

  • 34.

    Weisstein, E. The CRC Encyclopedia of Mathematics, 3rd ed.; Chapman and Hall/CRC Press: Boca Raton, FL, USA, 2009.

  • 35.

    Beyer, W.H. CRC Standard Mathematical Tables, 28th ed.; CRC Press: Boca Raton, FL, USA, 1987; p. 455.

  • 36.

    Shampine, L.F.; Reichelt, M.W. The MATLAB ODE suite. SIAM J. Sci. Comput. 1997, 18, 1–22.

  • 37.

    Abramowitz, M.; Stegun, I.A. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables; Dover Publications: New York, NY, USA, 1972.

  • 38.

    Madau, P.; Dickinson, M. Cosmic star formation history. Annu. Rev. Astron. Astrophys. 2014, 52, 415–486.

  • 39.

    Madau, P.; Fragos, T. Radiation backgrounds at cosmic dawn: X-rays from compact binaries. Astrophys. J. 2017, 840, 39.

  • 40.

    Labbe, I.; van Dokkum, P.; Nelson, E.; et al. A population of red candidate massive galaxies 600 Myr after the Big Bang. Nature 2023, 616, 266–269.

  • 41.

    Dahlen, T.; Mobasher, B.; Dickinson, M.; et al. Evolution of the luminosity function, star formation rate, morphology, and size of star-forming galaxies selected at rest-frame 1500 and 2800 Angstrom. Astrophys. J. 2007, 654, 172–185.

  • 42.

    Cucciati, O.; Tresse, L.; Ilbert, O.; et al. The star formation rate density and dust attenuation evolution over 12 Gyr with the VVDS surveys. Astron. Astrophys. 2012, 539, A31.

  • 43.

    Perez-Gonzalez, P.G.; Rieke, G.H.; Villar, V.; et al. The stellar mass assembly of galaxies from z = 0 to z = 4: Analysis of a sample selected in the rest-frame near-infrared with spitzer. Astrophys. J. 2008, 675, 234–261.

  • 44.

    Moustakas, J.; Coil, A.L.; Aird, J.; et al. PRIMUS: Constraints on star formation quenching and galaxy merging, and the evolution of the stellar mass function from z = 0–1. Astrophys. J. 2013, 767, 50.

  • 45.

    Bland-Hawthorn, J.; Gerhard, O. The galaxy in context: Structural, kinematic, and integrated properties. Annu. Rev. Astron. Astrophys. 2016, 54, 529–596.

  • 46.

    Catinella, B.; Saintonge, A.; Janowiecki, S.; et al. xGASS: Total cold gas scaling relations and molecular-to-atomic gas ratios of galaxies in the local Universe. Mon. Not. R. Astron. Soc. 2018, 476, 875–895.

  • 47.

    Calette, A.R.; Avila-Reese, V.; Rodr´ıguez-Puebla, A.; et al. The HI- and H2-to-Stellar Mass Correlations of Late- and Early-Type Galaxies and their Consistency with the Observational Mass Functions. Rev. Mex. Astron. Astrofis. 2018, 54, 443–483.

  • 48.

    Kroupa, P. On the variation of the initial mass function. Mon. Not. R. Astron. Soc. 2001, 322, 231–246.

  • 49.

    Chabrier, G. Galactic stellar and substellar initial mass function. Publ. Astron. Soc. Pac. 2003, 115, 763–795.

  • 50.

    Conroy, C. Modeling the panchromatic spectral energy distributions of galaxies. Annu. Rev. Astron. Astrophys. 2013, 51, 393–455.

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How to Cite
Kröger, M.; Schlickeiser, R. Compartmental Description of the Cosmological Baryonic Matter Cycle. Inclusion of Triggered Star Formation. Applied Mathematics and Statistics 2026, 3 (1), 6. https://doi.org/10.53941/ams.2026.100006.
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