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Abstract We further investigate the dark energy model based on the Finsler geometry inspired osculating Barthel–Kropina cosmology. The Barthel–Kropina cosmological approach is based on the introduction of a Barthel connection in an osculating Finsler geometry, with the connection having the property that it is the Levi-Civita connection of a Riemannian metric. From the generalized Friedmann equations of the Barthel–Kropina model, obtained by assuming that the background Riemannian metric is of the Friedmann–Lemaitre–Robertson–Walker type, an effective geometric dark energy component can be generated, with the effective, geometric type pressure, satisfying a linear barotropic type equation of state. The cosmological tests, and comparisons with observational data of this dark energy model are considered in detail. To constrain the Barthel–Kropina model parameters, and the parameter of the equation of state, we use 57 Hubble data points, and the Pantheon Supernovae Type Ia data sample. The st statistical analysis is performed by using Markov Chain Monte Carlo (MCMC) simulations. A detailed comparison with the standard $$\Lambda $$ Λ CDM model is also performed, with the Akaike information criterion (AIC), and the Bayesian information criterion (BIC) used as the two model selection tools. The statefinder diagnostics consisting of jerk and snap parameters, and the Om(z) diagnostics are also considered for the comparative study of the Barthel–Kropina and $$\Lambda $$ Λ CDM cosmologies. Our results indicate that the Barthel–Kropina dark energy model gives a good description of the observational data, and thus it can be considered a viable alternative of the $$\Lambda $$ Λ CDM model.

About 70% of the Universe is Dark Energy, but the physics community still does not know what it is. Delta gravity (DG) is an alternative theory of gravitation that could solve this cosmological problem. Previously, we studied the Universe’s accelerated expansion, where DG was able to explain the SNe-Ia data successfully. In this work, we computed the cosmological fluctuations in DG that give rise to the CMB through a hydrodynamic approximation. We calculated the gauge transformations for the metric and the perfect fluid to present the equations of the evolution of cosmological fluctuations. This provided the necessary equations to solve the scalar TT power spectrum in a semi-analytical way. These equations are useful for comparing the DG theory with astronomical observations and thus being able to constrain the DG cosmology.

A gravitational field model based on two symmetric tensors, g μ ν and g ˜ μ ν , is studied, using a Markov Chain Monte Carlo (MCMC) analysis with the most updated catalog of SN-Ia. In this model, new matter fields are added to the original matter fields, motivated by an additional symmetry ( δ ˜ symmetry). We call them δ ˜ matter fields. This theory predicts an accelerating Universe without the need to introduce a cosmological constant Λ by hand in the equations. We obtained a very good fit to the SN-Ia Data, and with this, we found the two free parameters of the theory called C and L 2 . With these values, we have fixed all the degrees of freedom in the model. The last H 0 local value measurement is in tension with the CMB Data from Planck. Based on an absolute magnitude M V = − 19.23 for the SN, Delta Gravity finds H 0 to be 74.47 ± 1.63 km/(s Mpc). This value is in concordance with the last measurement of the H 0 local value, 73.83 ± 1.48 km/(s Mpc).

This study introduces a method for identifying territories ideal for establishing photovoltaic (PV) plants for green hydrogen (GH2) production in the Antofagasta region of northern Chile, a location celebrated for its outstanding solar energy potential. Assessing the viability of PV plant installation necessitates a balanced consideration of technical aspects and socio-environmental constraints, such as the proximity to areas of ecological importance and indigenous communities, to identify potential zones for solar and non-conventional renewable energy (NCRE)-based hydrogen production.To tackle this challenge, we propose a methodology that utilizes geospatial analysis, integrating Geographic Information System (GIS) tools with sensitivity analysis, to determine the most suitable sites for PV plant installation in the Antofagasta region. Our geospatial analysis employs the QGIS software to identify these optimal locations, while sensitivity analysis uses the Sørensen–Dice coefficient method to assess the similarity among chosen socio-environmental variables.Applying this methodology to the Antofagasta region reveals that a significant area within a 15 km radius of existing road networks and electrical substations is favorable for photovoltaic projects. Our sensitivity analysis further highlights the limiting effects of socio-environmental factors and their interactions. Moreover, our research finds that enlarging areas of socio-environmental importance could increase the total hydrogen production by about 10% per commune, indicating the impact of these factors on the potential for renewable energy production.