DES Y1 results: Splitting growth and geometry to test 
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We present model independent reconstructions of quintessence and the swampland conjectures (SC) using both machine learning (ML) and cosmography. In particular, we demonstrate how the synergies between theoretical analyses and ML can provide key insights on the nature of dark energy and modified gravity. Using the Hubble parameter HðzÞ data from the cosmic chronometers we find that the ML and cosmography reconstructions of the SC are compatible with observations at low redshifts. Finally, including the growth rate data fσ 8 ðzÞ we perform a model independent test of modified gravity cosmologies through two phase diagrams, namely H − fσ 8 and η − fσ 8 , where the anisotropic stress parameter η is obtained via the E g statistics, which is related to gravitational lensing data. While the first diagram is consistent within the errors with the ΛCDM model, the second one has a ∼2σ deviation of the anisotropic stress from unity at z ∼ 0.3 and a ∼4σ deviation at z ∼ 0.9, thus pointing toward mild deviations from general relativity, which could be further tested with upcoming large-scale structure surveys.

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“…Overall, these results are consistent with Ref. [97] and the analysis of [98] from the DES Y1 release, where the FIG. 4.…”

Section: Phase Diagramssupporting

confidence: 91%

Machine learning and cosmographic reconstructions of quintessence and the swampland conjectures

Arjona

Nesseris

2021

Phys. Rev. D

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“…Its most notable achievement with a single dataset is the fit to the complicated cosmic microwave background power spectra with just 6 independent parameters [2]. Equally impressive is the fact that the same model fits a diverse set of measurements in the late-time universe, including probes of the expansion history and the growth of structure [47,[55][56][57][58]74]. As measurement precision has improved, while most cosmological data remain individually consistent with ΛCDM, tensions have emerged when comparing constraints on common parameters from the CMB versus late time observations.…”

Section: Discussionmentioning

confidence: 99%

Chameleon Early Dark Energy and the Hubble Tension

Karwal,

Raveri,

Jain

et al. 2021

Preprint

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Early dark energy (EDE) offers a particularly interesting theoretical approach to the Hubble tension, albeit one that introduces its own set of challenges, including a new 'why then' problem related to the EDE injection time at matter-radiation equality, and a mild worsening of the largescale structure (LSS) tension. Both these challenges center on the properties of dark matter, which becomes the dominant component of the Universe at EDE injection and is also responsible for seeding LSS. Motivated by this, we explore the potential of couplings between EDE and dark matter to address these challenges, focusing on a mechanism similar to chameleon dark energy theories, deeming this chameleon early dark energy (CEDE). We study the cosmological implications of such theories by fitting to the CMB, BAO, supernovae and the local value of H0. We find that the Hubble tension is resolved by CEDE with H0 = 71.19(71.85) ± 0.99 km/s/Mpc. Further, the model provides an excellent fit to all the data, with no change to the CMB χ 2 relative to a ΛCDM fit to just the CMB, BAO and SNe (i.e. excluding the H0 tension for ΛCDM). We find a mild preference (∼ 2σ) for the chameleon coupling constant β > 0.

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“…Previously, the DES Collaboration analyzed data from its first year of observations, which covered 1514 deg 2 , and constrained cosmological parameters using galaxy clustering and gravitational lensing in ΛCDM and wCDM [33], constrained beyond-wCDM models [106], and carried out numerous other tests of the standard cosmological framework [64,101,105,[107][108][109][110][111][112][113][114]. Along with the aforementioned KiDS and HSC observations and analyses, the DES Y1 analysis emphasized redundancy using two shape measurement methods that are independently calibrated, several photometric redshift estimation and validation techniques, and two in-dependent codes for predicting the measurements and performing a likelihood analysis.…”

Section: Introductionmentioning

confidence: 99%

Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing

Abbott,

Aguena,

Alarcon

et al. 2021

Preprint

Self Cite

128

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DES Y1 results: Splitting growth and geometry to test ΛCDM

Cited by 22 publications

References 137 publications

Machine learning and cosmographic reconstructions of quintessence and the swampland conjectures

Machine learning and cosmographic reconstructions of quintessence and the swampland conjectures

Chameleon Early Dark Energy and the Hubble Tension

Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing

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