In this work, we demonstrate that a dynamical dark energy component predicted by massive gravity gives rise to a distinctive evolution of the equation of state. This scenario is favoured over the standard $\Lambda$CDM model when confronted with the latest combined datasets from the Dark Energy Spectroscopic Instrument (DESI), the cosmic microwave background (CMB), and supernova observations. The model stands out as a rare example of a healthy, self-consistent theory that accommodates phantom dark energy while maintaining a technically natural, small asymptotic cosmological constant. Our analysis indicates a preferred graviton mass of approximately $5 \times 10^{-33} \text{eV}$, suggesting the emergence of a new cosmological length scale. This leads to a maximal deviation of the equation of state around $z \sim 2.5$, a prediction that will be robustly tested by upcoming, deeper surveys of baryon acoustic oscillations. less

Understanding the impact of neutrino masses on the evolution of Universe is a crucial aspect of modern cosmology. Due to their large free streaming lengths, neutrinos significantly influence the formation of cosmic structures at non-linear scales. To maximize the information yield from current and future galaxy surveys, it is essential to generate precise theoretical predictions of structure formation. One approach to achieve this is by running large sets of cosmological numerical simulations, which is a computationally intensive process. In this study, we propose a deep learning-based generative adversarial network (GAN) model to emulate the Universe for a variety of neutrino masses. Our model called $\nu$GAN (for neutrino GAN) is able to generate 2D cosmic webs of the Universe for a number of neutrino masses ranging from 0.0 eV to 0.4 eV. The generated maps exhibit statistical independence, lack correlations with training data, and very closely resemble the distribution of matter in true maps. We assess the accuracy of our results both visually and through key statistics used in cosmology and computer vision analyses. Our results indicate that samples generated by $\nu$GAN are accurate within a 5% error on power spectrum between k=0.01 to k=0.5 h/Mpc. Although this accuracy covers the mildly non-linear scales, consistent with other works and observations, achieving higher accuracy at fully non-linear scales requires more sophisticated models, such as diffusion models. Nevertheless, our work opens up new avenues for building emulators to generate fast and massive neutrino simulations, potentially revolutionizing cosmological predictions and analyses. This work serves as a proof-of-concept, paving the way for future extensions with higher-resolution 3D data and advanced generative models. less

This paper introduces a cosmological model that incorporates the simultaneous merger process for evolving dark energy and evolving dark matter and analyzes its Hubble parameter behavior. To validate this model, we assess the applicability of the generalized second law of thermodynamics and the maximum entropy condition within this framework. We derive a generalized form of the Hubble parameter for this model, demonstrating that it converges to the standard Hubble parameter in the non-merger case (\(\xi = 0\)). The merging model's equation of state parameters resembles those of evolving dark matter and dark energy, with \(w_c(z) \simeq w_{\rm dm} \simeq 0\) and \(w_v(z) \simeq w_{\rm de} \simeq -1\) at $z\rightarrow 0$, aligning with recent observations. We attribute the roles of dynamical dark matter and dark energy to super-voids and super-clusters, the largest merging objects in the web-like universe. We compare our model by analyzing the Hubble parameter and the entropy along with its first and second derivatives for the $w$CDM and standard $\Lambda$CDM models. Our plots indicate that the models incorporating only cluster mergers exhibit greater discrepancies with both observational Hubble parameters and the standard model at $z > 1$. A key finding is that in models featuring only cluster mergers, Hubble and entropy rates consistently decrease. Furthermore, we demonstrate that the $\Lambda$CDM model with both additive and non-additive entropy violates the convexity condition, whereas the merger voids model aligns with maximizing entropy and at the same time may help avert a \textit{Big Rip} scenario for our universe. less

In the early Universe, neutrinos decouple quickly from the primordial plasma and propagate without further interactions. The impact of free-streaming neutrinos is to create a temporal shift in the gravitational potential that impacts the acoustic waves known as baryon acoustic oscillations (BAOs), resulting in a non-linear spatial shift in the Fourier-space BAO signal. In this work, we make use of and extend upon an existing methodology to measure the phase shift amplitude $\beta_{\phi}$ and apply it to the DESI Data Release 1 (DR1) BAOs with an anisotropic BAO fitting pipeline. We validate the fitting methodology by testing the pipeline with two publicly available fitting codes applied to highly precise cubic box simulations and realistic simulations representative of the DESI DR1 data. We find further study towards the methods used in fitting the BAO signal will be necessary to ensure accurate constraints on $\beta_{\phi}$ in future DESI data releases. Using DESI DR1, we present individual measurements of the anisotropic BAO distortion parameters and the $\beta_{\phi}$ for the different tracers, and additionally a combined fit to $\beta_{\phi}$ resulting in $\beta_{\phi} = 2.7 \pm 1.7$. After including a prior on the distortion parameters from constraints using \textit{Planck} we find $\beta_{\phi} = 2.7^{+0.60}_{-0.67} $ suggesting $\beta_{\phi} > 0$ at 4.3$\sigma$ significance. This result may hint at a phase shift that is not purely sourced from the standard model expectation for $N_{\rm{eff}}$ or could be a upwards statistical fluctuation in the measured $\beta_{\phi}$; this result relaxes in models with additional freedom beyond $\Lambda$CDM. less

Strong degeneracy exists between some modified gravity (MG) models and massive neutrinos because the enhanced structure growth produced by modified gravity can be suppressed due to the free-streaming massive neutrinos. Previous works showed this degeneracy can be broken with non-Gaussian or velocity information. Therefore in this work, we focus on the large-scale structure (LSS) in redshift space and investigate for the first time the possibility of using the non-Gaussian information and velocity information captured by the 3D scalar Minkowski functionals (MFs) and the 3D Minkowski tensors (MTs) to break this degeneracy. Based on the Quijote and Quijote-MG simulations, we find the imprints on redshift space LSS left by the Hu-Sawicki $f(R)$ gravity can be discriminated from those left by massive neutrinos with these statistics. With the Fisher information formalism, we first show how the MTs extract information with their perpendicular and parallel elements for both low- and high-density regions; then we compare constraints from the power spectrum monopole and MFs in real space with those in redshift space, and investigate how the constraining power is further improved with anisotropies captured by the quadrupole and hexadecapole of the power spectrum and the MTs; finally, we combine the power spectrum multipoles with MFs plus MTs and find the constraints from the power spectrum multipoles on $\Omega_{\mathrm{m}}, h, \sigma_8$, $M_\nu$, and $f_{R_0}$ can be improved, because they are complemented with non-Gaussian information, by a factor of 3.4, 3.0, 3.3, 3.3, and 1.9 on small scales ($k_{\rm{max}}=0.5~h\rm{Mpc}^{-1},\ R_G=5~h^{-1}\rm{Mpc}$), and 2.8, 2.2, 3.4, 3.4, and 1.5 on large scales ($k_{\rm{max}}=0.25~h\rm{Mpc}^{-1},\ R_G=10~h^{-1}\rm{Mpc}$). less