Comparison of dark energy models using late-universe observations

In the framework of general relativity, dark energy was proposed to explain the cosmic acceleration. A pivotal inquiry in cosmology is to determine whether dark energy is the cosmological constant and, if not, the challenge lies in constraining how it evolves with time. In this paper, we utilize the latest observational data to constrain some typical dark energy models and make a comparison for them according to their capabilities of fitting the current data. Our study is confined to late-Universe observations, including the baryon acoustic oscillation, type Ia supernova, cosmic chronometer, and strong gravitational lensing time delay data. We employ the Akaike information criterion (AIC), deviance information criterion (DIC), and Bayesian information criterion (BIC) to assess the worth of models. The AIC and DIC analyses indicate that all dark energy models outperform the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. However, the BIC analysis leaves room for $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ due to its heavier penalty on the model complexity. Compared to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$, most dark energy models are robustly supported by AIC and DIC while being explicitly disfavored by BIC. The models that are robustly favored by AIC and DIC and not explicitly disfavored by BIC include the $w\mathrm{CDM}$, interacting dark energy, and Ricci dark energy models. Furthermore, we observe that an alternative modified gravity model exhibits superior performance when compared with $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ across all information criteria.

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Publication Details

Journal
Physical review. D/Physical review. D.
Published
2025-08-22
DOI
https://doi.org/10.1103/mcgb-ntwr
Citations
13
Primary Topic
Cosmology and Gravitation Theories
Type
article
Field-Weighted Citation Impact
15.01

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article

Comparison of dark energy models using late-universe observations

Peng-Ju Wu
13 citations
Physical review. D/Physical review. D.
Cosmology and Gravitation Theories
15.01
article

Comparison of dark energy models using late-universe observations

Peng-Ju Wu
article en
13 citations

Abstract

In the framework of general relativity, dark energy was proposed to explain the cosmic acceleration. A pivotal inquiry in cosmology is to determine whether dark energy is the cosmological constant and, if not, the challenge lies in constraining how it evolves with time. In this paper, we utilize the latest observational data to constrain some typical dark energy models and make a comparison for them according to their capabilities of fitting the current data. Our study is confined to late-Universe observations, including the baryon acoustic oscillation, type Ia supernova, cosmic chronometer, and strong gravitational lensing time delay data. We employ the Akaike information criterion (AIC), deviance information criterion (DIC), and Bayesian information criterion (BIC) to assess the worth of models. The AIC and DIC analyses indicate that all dark energy models outperform the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. However, the BIC analysis leaves room for $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ due to its heavier penalty on the model complexity. Compared to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$, most dark energy models are robustly supported by AIC and DIC while being explicitly disfavored by BIC. The models that are robustly favored by AIC and DIC and not explicitly disfavored by BIC include the $w\mathrm{CDM}$, interacting dark energy, and Ricci dark energy models. Furthermore, we observe that an alternative modified gravity model exhibits superior performance when compared with $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ across all information criteria.

Physical review. D/Physical review. D.Vol. 112(4)
Ningxia University (CN)
Ningxia University
Openalex Percentile: Top 1%
Cosmology and Gravitation Theories
15.01
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Comparison of dark energy models using late-universe observations — Peng-Ju Wu · Physical review. D/Physical review. D. (2025) | TGRS Research Map | TGRS