A possible late-time transition of $M_B$ inferred via neural networks

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 $M_B$ 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 $M_B$ 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 $z\approx 1$ region.

arXiv comment13 pages, 9 sets of figures, 2 tables. To appear in JCAP

References in corpus (117)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• Dynamics of dark energy
• The Cosmological Constant and Dark Energy
• Exploring the Expansion History of the Universe
• Accelerating Universes with Scaling Dark Matter
• 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
• Dark Matter Search Results from a One Tonne$\times$Year Exposure of XENON1T
• Results from a search for dark matter in the complete LUX exposure
• The Pantheon+ Analysis: Cosmological Constraints
• A 6% measurement of the Hubble parameter at $z\sim 0.45$: direct evidence of the epoch of cosmic re-acceleration
• Cosmic Chronometers: Constraining the Equation of State of Dark Energy. I: H(z) Measurements
• Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z$\sim$2
• Improved constraints on the expansion rate of the Universe up to z~1.1 from the spectroscopic evolution of cosmic chronometers
• The Pantheon+ Analysis: The Full Dataset and Light-Curve Release
• Challenges for $\Lambda$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 $H_0$
• The Atacama Cosmology Telescope: DR4 Maps and Cosmological Parameters
• Age-dating Luminous Red Galaxies observed with the Southern African Large Telescope
• Cosmology Intertwined II: The Hubble Constant Tension
• Probing the dark energy: methods and strategies
• Prospects for probing the dark energy via supernova distance measurements
• Cosmology Intertwined III: $f{\sigma }_8$ and $S_8$
• Reconstruction of dark energy and expansion dynamics using Gaussian processes
• Reconstructing the Cosmic Equation of State from Supernova distances
• On the Hubble constant tension in the SNe Ia Pantheon sample
• The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters
• How to determine an effective potential for a variable cosmological term
• Gaussian Process Cosmography
• Setting the Stage for Cosmic Chronometers. II. Impact of Stellar Population Synthesis Models Systematics and Full Covariance Matrix
• To H0 or not to H0?
• $H_0$ from cosmic chronometers and Type Ia supernovae, with Gaussian Processes and the novel Weighted Polynomial Regression method
• 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
• On the use of the local prior on the absolute magnitude of Type Ia supernovae in cosmological inference
• Smoothing Supernova Data to Reconstruct the Expansion History of the Universe and its Age
• 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
• An improved model-independent assessment of the late-time cosmic expansion
• Probing quintessence: reconstruction and parameter estimation from supernovae
• Model-independent reconstruction of $f(T)$ gravity from Gaussian Processes
• Model Independent Reconstruction of the Expansion History of the Universe and the Properties of Dark Energy
• Evidence for a Lower Value for $H_0$ from Cosmic Chronometers Data?
• Hints of FLRW Breakdown from Supernovae
• A new method to build the (inverse) distance ladder
• Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets
• TDCOSMO. XII. Improved Hubble constant measurement from lensing time delays using spatially resolved stellar kinematics of the lens galaxy
• A deep learning approach to cosmological dark energy models
• Quantifying the $S_8$ tension with the Redshift Space Distortion data set
• Fables of reconstruction: controlling bias in the dark energy equation of state
• Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia
• Hubble tension or a transition of the Cepheid SnIa calibrator parameters?
• Investigating dark energy experiments with principal components
• 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
• Model-independent test of the FLRW metric, the flatness of the Universe, and non-local measurement of $H_0{r}_{\mathrm{d}}$
• Cosmology Intertwined I: Perspectives for the Next Decade
• A model independent null test on the cosmological constant
• Evidence of a decreasing trend for the Hubble constant
• Phantom Dark Energy Models with a Nearly Flat Potential
• Model selection applied to reconstruction of the Primordial Power Spectrum
• On the homogeneity of SnIa absolute magnitude in the Pantheon+ sample
• Is cosmic acceleration proven by local cosmological probes?
• What can Machine Learning tell us about the background expansion of the Universe?
• 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
• Genetic Algorithms and Supernovae Type Ia Analysis
• BAO+BBN revisited -- Growing the Hubble tension with a 0.7km/s/Mpc constraint
• Falsifying $\Lambda$CDM: Model-independent tests of the concordance model with eBOSS DR14Q and Pantheon
• Reconstruction of the Dark Energy equation of state
• Gaussian processes and effective field theory of $f(T)$ gravity under the $H_0$ tension
• Parametric and nonparametric methods hint dark energy evolution
• Constraining Teleparallel Gravity through Gaussian Processes
• 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
• Non-parametric reconstruction of the cosmological \textit{jerk} parameter
• 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
• Assessment of the cosmic distance duality relation using Gaussian Process
• Revealing the late-time transition of $H_0$: relieve the Hubble crisis
• Quantifying the evidence for the current speed-up of the Universe with low and intermediate-redshift data. A more model-independent approach
• Hints of dark energy anisotropic stress using Machine Learning
• Reconstruction of the Dark Energy equation of state from latest data: the impact of theoretical priors
• Towards a model-independent reconstruction approach for late-time Hubble data
• A data-driven Reconstruction of Horndeski gravity via the Gaussian processes
• Reconstruction of the dark matter-vacuum energy interaction
• Revisiting a non-parametric reconstruction of the deceleration parameter from combined background and the growth rate data
• Cosmological parameter extraction and biases from type Ia supernova magnitude evolution
• 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 ${\Lambda }_{\mathrm{s}}$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
• Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia
• Neural Network Reconstruction of $H^{\prime }(z)$ and its application in Teleparallel Gravity
• Testing $\Lambda$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
• Joining bits and pieces of reionization history
• Reconstruction of late-time cosmology using Principal Component Analysis
• 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 $H_0$
• Constraints on cosmic curvature from cosmic chronometer and quasar observations

Cited by in corpus (7)

• 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 $H(z)$ 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