Cosmological Constraints on the DGP Model in light of DESI DR2 2025 Data

arXiv:2607.09240 · astro-ph.CO, gr-qc · Submitted 2026-07-10 · Read on arXiv

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Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: Next we'll be talking about the paper "Cosmological Constraints on the DGP Model in light of DESI DR2 2025 Data".

Jocelyn: The paper was written by the authors from.

Vera: Stay tuned as we take you through the paper and discuss its implications.

Jocelyn: We also have Subrahmanyan with us today — guest researcher.

Vera: Alright, let's get started.

Summary: Vera: So, we’ve seen how they’re combining all these powerful probes, and now the summary tells us what did they find when looking at both flat and non-flat versions of this model?

Jocelyn: The results are quite striking because the paper shows that for both scenarios, the inferred Hubble constant is significantly lower than what Planck measured.

Subrahmanyan: That’s a major finding; it suggests that the DGP framework doesn't really help resolve the persistent Hubble tension we see between early and late-time measurements.

Vera: It actually exacerbates the discrepancy, which is a huge deal for any theoretical model trying to explain our universe.

Jocelyn: And this leads into the overall conclusion that current observations strongly disfavor the DGP framework, doesn't it?

Subrahmanyan: That’s because of its inability to simultaneously accommodate both the DESI BAO constraints and the CMB constraints.

Vera: It’s a fundamental inconsistency in the theory itself when faced with this level of observational accuracy.

Jocelyn: The paper gives us some specific values, like H zero being around sixty-four km s-one Mpc-one for the non-flat case, which is much lower than expected.

Subrahmanyan: Those numbers are telling us that the model simply isn't matching what we see in reality across different cosmic epochs.

Vera: This summary really lays out a lot of trouble for the DGP model when you put all these high-precision data points together.

Improvements: Vera: We’ve established that the DGP model is struggling, but this study is doing something more than just showing failure; they're providing updated and more stringent limits.

Jocelyn: That's a huge improvement over previous work, using this high-precision DESI DR2 data as the cornerstone of the analysis.

Subrahmanyan: The researchers are refining how we test these models, moving away from older, less precise observational catalogs to a much more robust framework.

Vera: It’s about making sure that we're not just seeing a slight deviation but getting concrete limits on how far off the DGP model really is.

Jocelyn: And they are doing this by systematically running joint likelihood analyses for both flat and non-flat DGP models, which is quite detailed.

Subrahmanyan: The structural improvements in the analysis allow us to see exactly where the model breaks down, especially in how it handles expansion history H(z).

Vera: It’s not just that they're adding more data; they are fundamentally improving the way we interpret that data against a much tighter theoretical framework.

Jocelyn: So, we’ getting better constraints on the parameters like m and H zero because of this advanced methodology, which is great for us.

Subrahmanyin: It's a powerful combination of rigorous statistical methods and that enormous DESI dataset, yielding a much clearer picture than before any old studies.

Conclusion: Vera: As we wrap up this segment, we have seen the concrete results of the "Cosmological Constraints on the DGP Model in light of DESI DR2 two thousand twenty-five Data."

Jocelyn: The main takeaway is that both flat and non-flat DGP scenarios are heavily disfavored, with massive AIC values.

Subrahmanyan: This confirms that the DGP framework lacks the necessary flexibility to accommodate the constraints from DESI BAO and CMB simultaneously.

Vera: It's a powerful argument for ruling out a model that simply doesn't work with today’s observational technology, in essence.

Jocelyn: The delay in cosmic acceleration—that z t value being around zero point four one—is also a significant finding compared to what we expect from CDM.

Subrahmanyan: It shows that DGP is less efficient at driving the late-time acceleration we observe, which is a crucial physical implication for the model.

Vera: Looking ahead, this paper provides a very clear roadmap for what future models must overcome to be viable candidates for our universe.

Wrap Up: Vera: That was a fascinating deep dive into the findings of "Cosmological Constraints on the DGP Model in light of DESI DR2 two thousand twenty-five Data."

Jocelyn: It's really exciting to see how the incredible power of DESI is allowing us to push back against and refine our understanding of theoretical models like this.

Subrahmanyan: The implications are huge, showing that when we look at the universe with high precision, we have very little room for certain models that don't align with reality.

Vera: We’ve learned that while the DGP model might offer some theoretical elegance regarding extra dimensions, its failure to match our observed H zero and constraints is a deal-breaker.

Jocelyn: And it’s clear that the self-consistent calibration of the sound horizon is essential for any physical interpretation, preventing those unphysical results we saw earlier.

Subrahmanyan: We're now looking at a much more refined picture of cosmology, moving toward models that are truly robust against all competing data.

Vera: It’s definitely a turning point in how we approach modified gravity theories.

Jocelyn: A really important paper, it is, and one to end the discussion on the DGP model today.

astro-ph.CO, gr-qc

Submitted: 2026-07-10

Updated: 2026-08-25

Comments: 14 pages, 4 figures. Eur.Phys.J.C 86 (2026) 5, 558. Comments are welcome

DOI: 10.1140/epjc/s10052-026-15798-7

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 88/100

The gist: We present updated constraints on both flat and non-flat Dvali–Gabadadze–Porrati (DGP) cosmological models using the latest baryon acoustic oscillation (BAO) measurements from the Dark Energy

Key concepts

DGP Model
A theoretical cosmological framework discussed in the episode. The analysis suggests that this model lacks the flexibility needed to reconcile current high-precision observational data, particularly regarding expansion history.
Hubble Tension
A discrepancy observed between different measurements of the Hubble constant (H_zero). The episode notes that the DGP framework does not help resolve this tension and may even worsen it.
DESI BAO Constraints
Constraints derived from Baryon Acoustic Oscillations using data from the DESI survey. The episode states that the DGP model struggles to accommodate these specific observational constraints alongside CMB data.

Terminology

Summary

We present updated constraints on both flat and non-flat Dvali–Gabadadze–Porrati (DGP) cosmological models using the latest baryon acoustic oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument Data Release 2 (DESI DR2), in combination with cosmic chronometer (CC), Type Ia supernova (SNIa), and cosmic microwave background (CMB) distance priors.

The study provides a consolidated assessment of the viability of the DGP framework by incorporating high-precision DESI observations within a unified analysis framework, offering updated and more stringent limits on the scenario.

The results for both models are detailed as follows:

  • Non-flat DGP Model: We obtain H 0 = 64.05 plus or minus 0.27 km s-1 Mpc-1, m = 0.3264 plus or minus 0.0043, and k = 0.0088 plus or minus 0.

  • Flat DGP Model: The constraints are H 0 = 63.28 plus or minus 0.25 km s-1 Mpc-1 and m = 0.3303 plus or minus 0.0036, respectively.

A key finding across both scenarios is that the inferred Hubble constant is significantly lower than the Planck CDM value, which indicates that the DGP framework does not alleviate the Hubble tension. Furthermore, current observations strongly disfavor the DGP framework due to its inability to simultaneously accommodate DESI BAO and CMB constraints.

Improvements for AI systems

(Note: Since the input is a bibliography and not the full paper, I must infer the core scientific challenges from the cited literature, which heavily focus on Cosmology, Large-Scale Structure (LSS), Dark Energy dynamics, and Multi-Messenger Astrophysics. My improvements will therefore target advanced data inference and simulation capabilities.)

The primary opportunity lies in moving beyond traditional maximum likelihood estimation by developing highly integrated, physics-constrained AI architectures capable of fusing heterogeneous datasets (CMB, SN Ia, BAO surveys, Weak Lensing maps) while simultaneously correcting for systematic observational biases and non-linear structure formation effects.

Improvement: Develop a Graph Neural Network (GNN) framework structured as a Bayesian Hierarchical Model. This architecture treats different astronomical datasets and physical parameters as interconnected nodes, allowing the AI to jointly infer parameters rather than treating them sequentially.

Mechanism:

  • Nodes: Each dataset (e.g., Planck CMB spectrum, DESI galaxy clustering measurements, Pantheon SN Ia distance moduli) is represented as a node.

  • Edges: The connections (edges) between nodes are weighted by known physical correlations and systematic covariance matrices (e.g., the correlation between the matter density parameter m derived from CMB and the observed BAO scale).

  • Functionality: The GNN utilizes a Mixture of Experts (MoE) architecture within its inference layer. This allows it to dynamically activate specialized sub-networks (experts)—one for each distinct physical regime (e.g., one expert for linear perturbation theory, another for non-linear structure formation, and a third for transient phenomena like kilonova light curves).

What the Improved AI System Can Do:

  • Joint Parameter Estimation: Accurately constrain cosmological parameters (m, w(z), H 0) by simultaneously fitting constraints from disparate sources (e.g., resolving the tension between local H 0 measurements and CMB-derived values) while quantifying the total systematic uncertainty propagated through the entire system.

  • Systematic Bias Mitigation: Explicitly model and correct for known instrumental systematics (e.g., photometric redshift uncertainties, varying detector point spread functions) by training the GNN to identify deviations from expected physical relationships across datasets.

Abstract

We present updated constraints on both flat and non-flat Dvali-Gabadadze-Porrati (DGP) cosmological models using the latest baryon acoustic oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument Data Release 2 (DESI DR2), in combination with cosmic chronometer (CC), Type Ia supernova (SNIa), and cosmic microwave background (CMB) distance priors. For the non-flat DGP model, we obtain H 0 = 64.05 plus or minus 0.27,, m = 0.3264 plus or minus 0.0043, and k = 0.0088 plus or minus 0.0016, corresponding to a transition redshift z t about 0.41. For the flat case, the constraints are H 0 = 63.28 plus or minus 0.25, and m = 0.3303 plus or minus 0.0036. In both scenarios, the inferred Hubble constant is significantly lower than the Planck CDM value, indicating that the DGP framework does not alleviate the Hubble tension. Current observations strongly disfavor the DGP framework, primarily due to its inability to simultaneously accommodate DESI BAO and CMB constraints.By incorporating the latest high-precision DESI observations within a unified analysis framework, this work provides updated and more stringent limits on the DGP scenario, offering a consolidated assessment of its viability in the context of current cosmological data.

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