A possible late-time transition of inferred via neural networks
arXiv:2402.10502 · doi:10.1088/1475-7516/2024/09/060
Abstract
The strengthening of tensions in the cosmological parameters has led to a reconsideration of fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the region.
13 pages, 9 sets of figures, 2 tables. To appear in JCAP
References in corpus (66)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Dynamics of dark energy
- A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team
- In the Realm of the Hubble tension a Review of Solutions
- Results from a search for dark matter in the complete LUX exposure
- The Pantheon+ Analysis: Cosmological Constraints
- Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z2
- The Pantheon+ Analysis: The Full Dataset and Light-Curve Release
- Challenges for CDM: An update
- Cosmic Distances Calibrated to 1% Precision with Gaia EDR3 Parallaxes and Hubble Space Telescope Photometry of 75 Milky Way Cepheids Confirm Tension with LambdaCDM
- Reconstructing Dark Energy
- The trouble with
- On the Hubble constant tension in the SNe Ia Pantheon sample
- The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters
- Gaussian Process Cosmography
- To H0 or not to H0?
- On the evolution of the Hubble constant with the SNe Ia Pantheon Sample and Baryon Acoustic Oscillations: a feasibility study for GRB-cosmology in 2030
- Toward a Better Understanding of Cosmic Chronometers: A new measurement of H(z) at z~0.7
- Nonparametric Dark Energy Reconstruction from Supernova Data
- Thawing quintessence with a nearly flat potential
- Reconstruction of the deceleration parameter and the equation of state of dark energy
- The Pantheon+ Analysis: SuperCal-Fragilistic Cross Calibration, Retrained SALT2 Light Curve Model, and Calibration Systematic Uncertainty
- Nonparametric Reconstruction of the Dark Energy Equation of State
- Model Independent Reconstruction of the Expansion History of the Universe and the Properties of Dark Energy
- Hints of FLRW Breakdown from Supernovae
- TDCOSMO. XII. Improved Hubble constant measurement from lensing time delays using spatially resolved stellar kinematics of the lens galaxy
- Hubble tension or a transition of the Cepheid SnIa calibrator parameters?
- A new perspective on Dark Energy modeling via Genetic Algorithms
- Cosmic chronometers to calibrate the ladders and measure the curvature of the Universe. A model-independent study
- Evidence of a decreasing trend for the Hubble constant
- Model selection applied to reconstruction of the Primordial Power Spectrum
- Phantom Dark Energy Models with a Nearly Flat Potential
- Is cosmic acceleration proven by local cosmological probes?
- On the homogeneity of SnIa absolute magnitude in the Pantheon+ sample
- Model-independent reconstruction of the Interacting Dark Energy Kernel: Binned and Gaussian process
- How to use GP: Effects of the mean function and hyperparameter selection on Gaussian Process regression
- BAO+BBN revisited -- Growing the Hubble tension with a 0.7km/s/Mpc constraint
- Reconstruction of the Dark Energy equation of state
- Gaussian processes and effective field theory of gravity under the tension
- Parametric and nonparametric methods hint dark energy evolution
- Health checkup test of the standard cosmological model in view of recent Cosmic Microwave Background Anisotropies experiments
- Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
- Neural Network Reconstruction of Late-Time Cosmology and Null Tests
- Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data
- Reducing the uncertainty on the Hubble constant up to 35\% with an improved statistical analysis: different best-fit likelihoods for Supernovae Ia, Baryon Acoustic Oscillations, Quasars, and Gamma-Ray Bursts
- Revealing the late-time transition of : relieve the Hubble crisis
- Assessment of the cosmic distance duality relation using Gaussian Process
- Towards a model-independent reconstruction approach for late-time Hubble data
- A data-driven Reconstruction of Horndeski gravity via the Gaussian processes
- Measuring the sound horizon and absolute magnitude of SNIa by maximizing the consistency between low-redshift data sets
- Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
- Model independent bounds on Type Ia supernova absolute peak magnitude
- Transition dynamics in the CDM model: Implications for bound cosmic structures
- Non-parametric reconstruction of interaction in the cosmic dark sector
- Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation
- Neural Network Reconstruction of and its application in Teleparallel Gravity
- Testing CDM cosmology in a binned universe: anomalies in the deceleration parameter
- A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning
- Neural network reconstruction of cosmology using the Pantheon compilation
- LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
- Spatial Curvature and Thermodynamics
- Minimal model dependent constraints on cosmological nuisance parameters and cosmic curvature from combinations of cosmological data
- Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning
- Checking the second law at cosmic scales
- Non-Gaussian Likelihoods for Type Ia Supernovae Cosmology: Implications for Dark Energy and
- Constraints on cosmic curvature from cosmic chronometer and quasar observations
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- The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
- Model-independent cosmological inference post DESI DR1 BAO measurements
- Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
- Gamma-Ray Bursts Calibrated from the Observational Data in Artificial Neural Network Framework
- Effect of Peak Absolute Magnitude of Type Ia Supernovae and Sound Horizon Values on Hubble Tension using DESI results
- What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?
- Testing the Distance Duality Relation with Cosmological Observations at high Redshift using Artificial Neural Network