Hints of dark energy anisotropic stress using Machine Learning

Abstract

Recent analyses of the Planck data and quasars at high redshifts have suggested possible deviations from the flat $\Lambda$ cold dark matter model ($\Lambda$CDM), where $\Lambda$ is the cosmological constant. Here we use machine learning methods to investigate any possible deviations from $\Lambda$CDM at both low and high redshifts by using the latest cosmological data. Specifically, we apply the Genetic Algorithms to explore the nature of dark energy (DE) in a model independent fashion by reconstructing its equation of state $w(z)$, the growth index of matter density perturbations $\gamma (z)$, the linear DE anisotropic stress ${\eta }_{\text{DE}}(z)$ and the adiabatic sound speed $c_{\text{s,DE}}^2(z)$ of DE perturbations. We find a $\sim 2\sigma$ deviation of $w(z)$ from -1 at high redshifts, the adiabatic sound speed is negative at the $\sim 2.5\sigma$ level at $z=0.1$ and a $\sim 2\sigma$ deviation of the anisotropic stress from unity at low redshifts and $\sim 4\sigma$ at high redshifts. These results hint towards either the presence of an non-adiabatic component in the DE sound speed or the presence of DE anisotropic stress, thus hinting at possible deviations from the $\Lambda$CDM model.

28 pages, 6 figures, 3 tables, changes match published version

References in corpus (30)

• Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics Beyond LambdaCDM
• The Clustering of the SDSS DR7 Main Galaxy Sample I: A 4 per cent Distance Measure at z=0.15
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z$\sim$2
• Supernova Constraints and Systematic Uncertainties from the First 3 Years of the Supernova Legacy Survey
• Baryon Acoustic Oscillations in the Lyα forest of BOSS DR11 quasars
• Two new diagnostics of dark energy
• Planck evidence for a closed Universe and a possible crisis for cosmology
• The Expansion of the Universe is Faster than Expected
• H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model
• A discriminating probe of gravity at cosmological scales
• Confirmation of general relativity on large scales from weak lensing and galaxy velocities
• Testing cosmological structure formation using redshift-space distortions
• Gaussian Process Cosmography
• A f(R) gravity without cosmological constant
• J-PAS: The Javalambre-Physics of the Accelerated Universe Astrophysical Survey
• An exact mapping between the Variational Renormalization Group and Deep Learning
• Constraining Dark Energy Anisotropic Stress
• A new perspective on Dark Energy modeling via Genetic Algorithms
• Enabling real-time multi-messenger astrophysics discoveries with deep learning
• A Lockdown Perspective on the Hubble Tension (with comments from the SH0ES team)
• Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes
• Connecting early and late epochs by f(z)CDM cosmography
• Is the Hubble diagram of quasars in tension with concordance cosmology?
• Machine Learning meets the redshift evolution of the CMB Temperature
• Cosmological constraints on non-adiabatic dark energy perturbations
• A novel null test for the $\Lambda$CDM model with growth-rate data
• The WiggleZ Dark Energy Survey: constraining the evolution of Newton's constant using the growth rate of structure
• Reconstruction of the null-test for the matter density perturbations
• Genetic algorithms in astronomy and astrophysics

Cited by in corpus (35)

• Hints of a Local Matter Underdensity or Modified Gravity in the Low $z$ Pantheon data
• Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes
• Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
• Constraints on the distance duality relation with standard sirens
• Exhaustive Symbolic Regression
• Cosmographic parameters in model-independent approaches
• Neural Network Reconstruction of Late-Time Cosmology and Null Tests
• Inferring $S_8(z)$ and $\gamma (z)$ with cosmic growth rate measurements using machine learning
• Machine learning constraints on deviations from general relativity from the large scale structure of the Universe
• Machine Learning improved fits of the sound horizon at the baryon drag epoch
• Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
• Euclid: constraining dark energy coupled to electromagnetism using astrophysical and laboratory data
• Euclid: Forecast constraints on consistency tests of the $\Lambda$CDM model
• The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
• Hints for possible low redshift oscillation around the best fit $\Lambda$CDM model in the expansion history of the Universe
• Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures
• Measuring anisotropic stress with relativistic effects
• Testing the $\Lambda$CDM paradigm with growth rate data and machine learning
• Machine learning cosmic inflation
• Using machine learning to compress the matter transfer function $T(k)$
• A GREAT model comparison against the cosmological constant
• A possible late-time transition of $M_B$ inferred via neural networks
• Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
• Cosmological constraints with the Effective Fluid approach for Modified Gravity
• Non-parametric modeling of the cosmological data, base on the ${\chi }^2$ distribution
• Machine learning unveils the linear matter power spectrum of modified gravity
• Complementary consistency test of the Copernican principle via Noether's theorem and machine learning forecasts
• Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models
• A varying Dark Energy effective speed of sound parameter in the phantom Universe
• Effects of Dark Energy anisotropic stress on the matter power spectrum
• Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning
• Forecast constraints on null tests of the $\Lambda$CDM model with SPHEREx
• Bayesian and Machine-Learning Analyses of Nonminimal $f(Q)$ Gravity and $H_0$ Tension
• $G_3$ -- interacting scalar tensor dark energy
• A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation