Forecast constraints on null tests of the $\Lambda$CDM model with SPHEREx

Abstract

In this work we quantify the ability of the upcoming SPHEREx survey to constrain cosmological observables and test the internal consistency of the cosmological constant and cold dark matter ($\Lambda$CDM) model. Using Fisher matrix forecasting, we assess the expected precision on Baryon Acoustic Oscillations (BAO) observables, such as the angular diameter distance $D_{\mathrm{A}}(z)$ and the Hubble parameter $H(z)$. We further explore SPHEREx's potential to probe some of the fundamental assumptions of large-scale spatial homogeneity and isotropy, through model-independent reconstructions of several consistency tests of the $\Lambda$CDM model. In addition, we also examine the effect of the model dependence of the resulting Fisher and covariance matrices, using a neural network (NN) classification approach. We find that, while it is commonly assumed the covariance matrix depends weakly on the model, in fact the NN can very accurately ($\sim 98\%$) detect the underlying fiducial cosmological model based solely on the covariance matrix of the data, thus challenging this assumption. This model dependence, often neglected in standard analyses, can be naturally incorporated within simulation-based inference frameworks, which offer a flexible alternative for capturing such effects.

23 pages, 5 figures, 3 tables, comments welcome

References in corpus (17)

• Two new diagnostics of dark energy
• Euclid. I. Overview of the Euclid mission
• DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints
• Avoiding Dark Energy with 1/R Modifications of Gravity
• Matter density perturbations and effective gravitational constant in modified gravity models of dark energy
• A general test of the Copernican Principle
• Fast likelihood-free cosmology with neural density estimators and active learning
• Using H(z) data as a probe of the concordance model
• A new perspective on Dark Energy modeling via Genetic Algorithms
• A new diagnostic for the null test of dynamical dark energy in light of DESI 2024 and other BAO data
• The radial BAO scale and Cosmic Shear, a new observable for Inhomogeneous Cosmologies
• Machine Learning improved fits of the sound horizon at the baryon drag epoch
• A novel null test for the $\Lambda$CDM model with growth-rate data
• Improved null tests of $\Lambda$CDM and FLRW in light of DESI DR2
• Machine learning cosmic inflation
• Using machine learning to compress the matter transfer function $T(k)$
• New perspectives on future rip scenarios with holographic dark energy

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