https://doi.org/10.1140/epjc/s10052-024-13146-1
Regular Article - Theoretical Physics
Can we learn from matter creation to solve the 
tension problem?
1
Institute of Space Sciences (ICE-CSIC), C. Can Magrans s/n, 08193, Barcelona, Spain
2
Lab. for Theor. Cosmology, International Centre of Gravity and Cosmos, Tomsk State University of Control Systems and Radio Electronics (TUSUR), 634050, Tomsk, Russia
3
ICREA, Passeig Luis Companys, 23, 08010, Barcelona, Spain
Received:
8
February
2024
Accepted:
21
July
2024
Published online:
6
August
2024
The tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate being of pure phenomenological nature. Bayesian (probabilistic) Machine Learning is used to learn the constraints on the free parameters of the models, with the learning being based on the generated expansion rate, H(z). Taking advantage of the method, the constraints for three redshift ranges are learned. Namely, for the two redshift ranges: ![$$z\in [0,2]$$](/articles/epjc/abs/2024/08/10052_2024_Article_13146/10052_2024_Article_13146_tex_eq4.png)
(cosmic chronometers) and ![$$z\in [0,2.5]$$](/articles/epjc/abs/2024/08/10052_2024_Article_13146/10052_2024_Article_13146_tex_eq5.png)
(cosmic chronometers + BAO), covering already available H(z) data, to validate the learned results; and for a third redshift interval, ![$$z\in [0,5]$$](/articles/epjc/abs/2024/08/10052_2024_Article_13146/10052_2024_Article_13146_tex_eq6.png)
, for forecasting purposes. It is learned that the 
term in the creation rate provides options that have the potential to solve the tension problem.
© The Author(s) 2024
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