Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

Abstract

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.

15 pages, 6 figures, changes match published version

References in corpus (102)

• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• Laser Interferometer Space Antenna
• A gravitational-wave standard siren measurement of the Hubble constant
• Science Case for the Einstein Telescope
• Dark Energy after GW170817: dead ends and the road ahead
• Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo
• Physics, Astrophysics and Cosmology with Gravitational Waves
• Strong constraints on cosmological gravity from GW170817 and GRB 170817A
• Dark Energy after GW170817 and GRB170817A
• Tests of General Relativity with the Binary Black Hole Signals from the LIGO-Virgo Catalog GWTC-1
• Detecting a stochastic background of gravitational radiation: Signal processing strategies and sensitivities
• Implications of the Neutron Star Merger GW170817 for Cosmological Scalar-Tensor Theories
• Tests of General Relativity with GW170817
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
• Reconstruction of dark energy and expansion dynamics using Gaussian processes
• A 2 per cent Hubble constant measurement from standard sirens within 5 years
• Wave Effects in Gravitational Lensing of Gravitational Waves from Chirping Binaries
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• H0LiCOW I. $H_0$ Lenses in COSMOGRAIL's Wellspring: Program Overview
• Breaking a Dark Degeneracy with Gravitational Waves
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Gaussian Process Cosmography
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Challenges to Self-Acceleration in Modified Gravity from Gravitational Waves and Large-Scale Structure
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Determination of Dark Energy by the Einstein Telescope: Comparing with CMB, BAO and SNIa Observations
• Parametrized tests of post-Newtonian theory using Advanced LIGO and Einstein Telescope
• Nonparametric Dark Energy Reconstruction from Supernova Data
• Cosmic distance-duality as probe of exotic physics and acceleration
• Search for gravitational lensing signatures in LIGO-Virgo binary black hole events
• Gravitational lensing of gravitational waves: A statistical perspective
• Effect of gravitational lensing on the distribution of gravitational waves from distant binary black hole mergers
• Probing the theory of gravity with gravitational lensing of gravitational waves and galaxy surveys
• Enhancing Gravitational-Wave Science with Machine Learning
• Gravitational-Wave Fringes at LIGO: Detecting Compact Dark Matter by Gravitational Lensing
• Nonparametric Reconstruction of the Dark Energy Equation of State
• Precise LIGO Lensing Rate Predictions for Binary Black Holes
• Gravitational Lensing of Gravitational Waves from Merging Neutron Star Binaries
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Speed of Gravitational Waves from Strongly Lensed Gravitational Waves and Electromagnetic Signals
• Phase effects from strong gravitational lensing of gravitational waves
• Cosmography with strong lensing of LISA gravitational wave sources
• A model-independent determination of the Hubble constant from lensed quasars and supernovae using Gaussian process regression
• Detecting Lensing-Induced Diffraction in Astrophysical Gravitational Waves
• Constraints on the interacting vacuum -- geodesic CDM scenario
• Curvature vs Distances: testing the FLRW cosmology
• Discovering intermediate-mass black hole lenses through gravitational wave lensing
• Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets
• Strong gravitational lensing of gravitational waves from double compact binaries - perspectives for the Einstein Telescope
• A new perspective on Dark Energy modeling via Genetic Algorithms
• The Distance Duality Relation from Strong Gravitational Lensing
• Deep-Learning continuous gravitational waves
• Strong lensing of gravitational waves as seen by LISA
• A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms
• Enabling real-time multi-messenger astrophysics discoveries with deep learning
• Gravitational wave lensing beyond general relativity: birefringence, echoes and shadows
• A model independent null test on the cosmological constant
• Multi-messenger tests of gravity with weakly lensed gravitational waves
• What can Machine Learning tell us about the background expansion of the Universe?
• Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes
• The blackness of the cosmic microwave background spectrum as a probe of the distance-duality relation
• Comparison of Standard Ruler and Standard Candle constraints on Dark Energy Models
• How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
• A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
• Genetic Algorithms and Supernovae Type Ia Analysis
• The Impact of Cosmic Dust on Supernova Cosmology
• Probing the cosmic distance duality with strong gravitational lensing and supernovae Ia data
• Gaussian processes reconstruction of dark energy from observational data
• Constraints on the distance duality relation with standard sirens
• Gaussian processes reconstruction of modified gravitational wave propagation
• Genetic Algorithms and Experimental Discrimination of SUSY Models
• Comparative analysis of model-independent methods for exploring the nature of dark energy
• Probing the distance-duality relation with high-$z$ data
• Reconstruction of the Dark Energy equation of state from latest data: the impact of theoretical priors
• Hints of dark energy anisotropic stress using Machine Learning
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Probing cosmic opacity at high redshifts with gamma-ray bursts
• New constraints on the distance duality relation from the local data
• Inferring the lensing rate of LIGO-Virgo sources from the stochastic gravitational wave background
• Testing the distance duality relation using type Ia supernovae and ultracompact radio sources
• Testing the distance duality relation with present and future data
• Statistical Test of Distance--Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations
• The Role of Machine Learning in the Next Decade of Cosmology
• Modelling gravitational waves from precessing black-hole binaries: Progress, challenges and prospects
• Predicting dark matter halo formation in N-body simulations with deep regression networks
• Machine Learning meets the redshift evolution of the CMB Temperature
• Regression methods in waveform modeling: a comparative study
• Latest evidence for a late time vacuum -- geodesic CDM interaction
• Limiting the effects of earthquakes on gravitational-wave interferometers
• A new Tolman test of a cosmic distance duality relation at 21 cm
• Constraining axionlike particles using the distance-duality relation
• Testing distance duality with CMB anisotropies
• The Power of Genetic Algorithms: what remains of the pMSSM?
• Analysis of beam position monitor requirements with Bayesian Gaussian regression
• Strongly lensed gravitational waves as the probes to test the cosmic distance duality relation
• Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
• Strong and weak lensing of Gravitational Waves: a semi-analytical approach
• A new method to test the cosmic distance duality relation using the strongly lensed gravitational waves
• $H_0$ from cosmic chronometers and Type Ia supernovae, with Gaussian processes and the weighted polynomial regression method
• Time Delay Analysis of the Lensed Quasar SDSS J1001+5027

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