Observational constraints on a spinor field generalized Chaplygin gas model in a spherically symmetric FLRW spacetime

Despite the remarkable success of the standard ΛCDM model in describing the evolution of the universe, several unresolved issues remain, such as the true nature of dark energy, fine-tuning problems, and the persistent Hubble tension. Motivated by these shortcomings, we construct a spinor field-based Generalized Chaplygin Gas (GCG) model that unifies dark matter and dark energy within a spherically symmetric Friedmann Lemaître Robertson Walker (FLRW) spacetime. This framework incorporates a nonlinear spinor field and considers an open universe geometry. We constrain the model parameters using the latest observational datasets, including Type Ia supernovae from the binned Pantheon compilation, Hubble parameter measurements from cosmic chronometers (CC) and SDSS, including baryon acoustic oscillation (BAO) data. Employing Markov chain Monte Carlo (MCMC) sampling techniques, we obtain best-fit values that indicate the spinor GCG model provides a competitive and viable alternative to the ΛCDM, particularly in the late-time universe. Furthermore, the model predicts a lower present-day Hubble constant, offering a potential resolution to the Hubble tension. The results highlight the rich phenomenology of spinor fields and their possible role in the dynamics of dark energy through spacetime interaction. © 2025 Elsevier B.V., All rights reserved.

Авторы
Goray Mahendra 1 , Saha Bijan 2, 3
Издательство
Springer-Verlag GmbH
Номер выпуска
10
Язык
Английский
Статус
Опубликовано
Номер
1146
Том
85
Год
2025
Организации
  • 1 Department of Physics, Sido Kanhu Murmu University, Dumka, India
  • 2 Joint Institute for Nuclear Research, Dubna, Dubna, Russian Federation
  • 3 RUDN University, Moscow, Russian Federation
Ключевые слова
Dark energy; Dark Matter; Galaxies; Gas dynamics; Hadrons; Markov chains; Chaplygin gas; Fine tuning; Gas models; Modeling parameters; Spacetime; Spinors; Symmetrics; Tuning problems; Supernovae
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