arXiv:2407.20285v1 Announce Type: new
Abstract: The $H_{0}$ 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: $zin [0,2]$~(cosmic chronometers) and $zin [0,2.5]$~(cosmic chronometers + BAO), covering already available $H(z)$ data, to validate the learned results; and for a third redshift interval, $zin[0,5]$, for forecasting purposes. It is learned that the $3alpha H_{0}$ term in the creation rate provides options that have the potential to solve the $H_{0}$ tension problem.
The study examines the $H_{0}$ tension problem in the context of a matter creation mechanism, which is a possible replacement for dark energy. The approach used is purely phenomenological, and Bayesian Machine Learning is employed to learn the constraints on the free parameters of the models based on the expansion rate, $H(z)$. The study focuses on three redshift ranges: $zin [0,2]$ and $zin [0,2.5]$, which cover available $H(z)$ data for validation, and a third redshift interval, $zin[0,5]$, for forecasting purposes. The study finds that the alpha H_{0}$ term in the creation rate offers potential solutions to the $H_{0}$ tension problem.
Future Roadmap:
- Validation: The next step for readers is to validate the learned results of the constraints for the redshift ranges $zin [0,2]$ and $zin [0,2.5]$. It is important to examine the available $H(z)$ data and compare it with the learned constraints to ensure the accuracy of the predictions.
- Forecasting: After validating the learned results, the study suggests exploring the third redshift interval $zin[0,5]$ for forecasting purposes. By using the generated expansion rate, $H(z)$, it will be possible to predict the behavior of the matter creation mechanism in this range and potentially gain insights into future observations.
- Challenges: One challenge that may arise is the availability and accuracy of the $H(z)$ data for validation and forecasting. Ensuring the reliability of the data used in the models is crucial for obtaining meaningful conclusions. Additionally, the phenomenological nature of the matter creation mechanism may introduce uncertainties and limitations in its applicability.
- Opportunities: The study presents an opportunity to further investigate and understand the $H_{0}$ tension problem. By exploring the potential solutions offered by the alpha H_{0}$ term in the creation rate, researchers can contribute to resolving this long-standing issue in cosmology. Moreover, using Bayesian Machine Learning allows for a probabilistic approach, which can provide valuable insights into the constraints and uncertainties of the models.
Overall, the future roadmap involves validating the learned results, exploring forecasting opportunities, overcoming challenges related to data availability and phenomenological nature of the mechanism, and leveraging the potential of Bayesian Machine Learning to gain insights into the $H_{0}$ tension problem and its potential solutions.