Cosmo-Learn: code for learning cosmology using different methods and mock data | Paper Citation Record

Observation 901a8c8f-badf-4cc3-a274-633af700fde1 · outbound

This paper cites Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant.

Cosmo-Learn: code for learning cosmology using different methods and mock data Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant

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Observation 590b75ce-8ce9-4c17-b3d1-b42aabc4686f · outbound

This paper cites Measurements of Omega and Lambda from 42 High-Redshift Supernovae.

Cosmo-Learn: code for learning cosmology using different methods and mock data Measurements of Omega and Lambda from 42 High-Redshift Supernovae

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Observation 005c62d1-d380-41a7-8571-9bb6333f64f2 · outbound

This paper cites In the Realm of the Hubble tension $-$ a Review of Solutions.

Cosmo-Learn: code for learning cosmology using different methods and mock data In the Realm of the Hubble tension $-$ a Review of Solutions

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This paper cites Baudis,Dark matter detection, J.

Cosmo-Learn: code for learning cosmology using different methods and mock data Baudis,Dark matter detection, J

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This paper cites Particle Dark Matter: Evidence, Candidates and Constraints.

Cosmo-Learn: code for learning cosmology using different methods and mock data Particle Dark Matter: Evidence, Candidates and Constraints

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This paper cites Gaitskell,Direct detection of dark matter, Ann.

Cosmo-Learn: code for learning cosmology using different methods and mock data Gaitskell,Direct detection of dark matter, Ann

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Observation 5f55afa7-f961-4ffc-bec3-84f9bf7fe4d3 · outbound

This paper cites The Cosmological Constant and Dark Energy.

Cosmo-Learn: code for learning cosmology using different methods and mock data The Cosmological Constant and Dark Energy

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Cosmo-Learn: code for learning cosmology using different methods and mock data Dynamics of dark energy

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This paper cites B-modes and the Nature of Inflation.

Cosmo-Learn: code for learning cosmology using different methods and mock data B-modes and the Nature of Inflation

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Observation 443ef6ce-5f60-45da-9b21-e95e06a76993 · outbound

This paper cites Probing Cosmic Inflation with the LiteBIRD Cosmic Microwave Background Polarization Survey.

Cosmo-Learn: code for learning cosmology using different methods and mock data Probing Cosmic Inflation with the LiteBIRD Cosmic Microwave Background Polarization Survey

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Observation 0e46bac1-d103-4320-82d4-735c7ec5a95c · outbound

This paper cites Cosmology Intertwined II: The Hubble Constant Tension.

Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmology Intertwined II: The Hubble Constant Tension

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Observation a273227f-f74c-4b0e-a4cf-33061fd46fde · outbound

This paper cites Cosmology Intertwined III: $f \sigma_8$ and $S_8$.

Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmology Intertwined III: $f \sigma_8$ and $S_8$

Reference 13

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Observation ff6ed6a8-dd08-4c8a-954b-b8b6a015ccba · outbound

This paper cites Quantifying the $S_8$ tension with the Redshift Space Distortion data set.

Cosmo-Learn: code for learning cosmology using different methods and mock data Quantifying the $S_8$ tension with the Redshift Space Distortion data set

Reference 14

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Observation 48294008-7220-4bb9-bda3-da333c2bad7a · outbound

This paper cites The Sigma-8 Tension is a Drag.

Cosmo-Learn: code for learning cosmology using different methods and mock data The Sigma-8 Tension is a Drag

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Observation 9ba126d4-82a5-418c-9572-e0f502a8e184 · outbound

This paper cites Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data.

Cosmo-Learn: code for learning cosmology using different methods and mock data Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

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Observation a4675ef6-6d1f-4f68-b9d9-c1ff072752d7 · outbound

This paper cites The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics.

Cosmo-Learn: code for learning cosmology using different methods and mock data The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics

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Observation 1c550579-4af3-4543-8b6d-3b7e77af3ffa · outbound

This paper cites Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies.

Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies

Reference 18

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This paper cites 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.

Cosmo-Learn: code for learning cosmology using different methods and mock data 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

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This paper cites H0LiCOW XIII. A 2.4% measurement of $H_{0}$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes.

Cosmo-Learn: code for learning cosmology using different methods and mock data H0LiCOW XIII. A 2.4% measurement of $H_{0}$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes

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Observation 83cf11c4-9179-4d04-96ea-e1f7ebcf85ae · outbound

This paper cites Small amplitude red giants elucidate the nature of the Tip of the Red Giant Branch as a standard candle.

Cosmo-Learn: code for learning cosmology using different methods and mock data Small amplitude red giants elucidate the nature of the Tip of the Red Giant Branch as a standard candle

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Cosmo-Learn: code for learning cosmology using different methods and mock data Unresolved cited work

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This paper cites Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing.

Cosmo-Learn: code for learning cosmology using different methods and mock data Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing

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Observation d64252b6-6fa0-4250-9207-e5ab693d7b69 · outbound

This paper cites The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological Implications from two Decades of Spectroscopic Surveys at the Apache Point observatory.

Cosmo-Learn: code for learning cosmology using different methods and mock data The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological Implications from two Decades of Spectroscopic Surveys at the Apache Point observatory

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Observation 91317820-63b6-4807-a252-9da4b54f2ceb · outbound

This paper cites BOSS Correlation Function Analysis from the Effective Field Theory of Large-Scale Structure.

Cosmo-Learn: code for learning cosmology using different methods and mock data BOSS Correlation Function Analysis from the Effective Field Theory of Large-Scale Structure

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Observation d648fcad-6a0c-4dd0-a09d-7432724d1969 · outbound

This paper cites One percent determination of the primordial deuterium abundance.

Cosmo-Learn: code for learning cosmology using different methods and mock data One percent determination of the primordial deuterium abundance

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This paper cites The Ups and Downs of Early Dark Energy solutions to the Hubble tension: a review of models, hints and constraints circa 2023.

Cosmo-Learn: code for learning cosmology using different methods and mock data The Ups and Downs of Early Dark Energy solutions to the Hubble tension: a review of models, hints and constraints circa 2023

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Observation 8618b038-edd5-449b-8bf7-187f9605fcae · outbound

This paper cites Neutrino Mass Bounds in the era of Tension Cosmology.

Cosmo-Learn: code for learning cosmology using different methods and mock data Neutrino Mass Bounds in the era of Tension Cosmology

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Observation 3ee8582c-d67b-4b24-8866-fa60837b5829 · outbound

This paper cites Quantum gravity phenomenology at the dawn of the multi-messenger era -- A review.

Cosmo-Learn: code for learning cosmology using different methods and mock data Quantum gravity phenomenology at the dawn of the multi-messenger era -- A review

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Observation 1fd8f3b8-e9e6-412e-a8f6-828ffbd6fc0f · outbound

This paper cites Modified Gravity and Cosmology: An Update by the CANTATA Network.

Cosmo-Learn: code for learning cosmology using different methods and mock data Modified Gravity and Cosmology: An Update by the CANTATA Network

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Observation b6b7e665-7dac-436e-9df1-75a71d078531 · outbound

This paper cites Teleparallel Gravity: From Theory to Cosmology.

Cosmo-Learn: code for learning cosmology using different methods and mock data Teleparallel Gravity: From Theory to Cosmology

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Observation a013cfb8-1616-4c36-80eb-b80c1c9f2681 · outbound

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Cosmo-Learn: code for learning cosmology using different methods and mock data Dark energy cosmology: the equivalent description via different theoretical models and cosmography tests

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Cosmo-Learn: code for learning cosmology using different methods and mock data Unified cosmic history in modified gravity: from F(R) theory to Lorentz non-invariant models

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Cosmo-Learn: code for learning cosmology using different methods and mock data Modified Gravity Theories on a Nutshell: Inflation, Bounce and Late-time Evolution

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Cosmo-Learn: code for learning cosmology using different methods and mock data Model independent tests of the standard cosmological model

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Cosmo-Learn: code for learning cosmology using different methods and mock data Reconstruction of dark energy and expansion dynamics using Gaussian processes

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This paper cites Null tests of the cosmological constant using supernovae.

Cosmo-Learn: code for learning cosmology using different methods and mock data Null tests of the cosmological constant using supernovae

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This paper cites Dynamical dark energy in light of the latest observations.

Cosmo-Learn: code for learning cosmology using different methods and mock data Dynamical dark energy in light of the latest observations

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Cosmo-Learn: code for learning cosmology using different methods and mock data Parametric and nonparametric methods hint dark energy evolution

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Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmic Chronometers: Constraining the Equation of State of Dark Energy. I: H(z) Measurements

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Cosmo-Learn: code for learning cosmology using different methods and mock data Improved constraints on the expansion rate of the Universe up to z~1.1 from the spectroscopic evolution of cosmic chronometers

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Cosmo-Learn: code for learning cosmology using different methods and mock data Four New Observational $H(z)$ Data From Luminous Red Galaxies of Sloan Digital Sky Survey Data Release Seven

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This paper cites Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z$\sim$2.

Cosmo-Learn: code for learning cosmology using different methods and mock data Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z$\sim$2

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This paper cites A 6% measurement of the Hubble parameter at $z\sim0.45$: direct evidence of the epoch of cosmic re-acceleration.

Cosmo-Learn: code for learning cosmology using different methods and mock data A 6% measurement of the Hubble parameter at $z\sim0.45$: direct evidence of the epoch of cosmic re-acceleration

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This paper cites Age-dating Luminous Red Galaxies observed with the Southern African Large Telescope.

Cosmo-Learn: code for learning cosmology using different methods and mock data Age-dating Luminous Red Galaxies observed with the Southern African Large Telescope

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Cosmo-Learn: code for learning cosmology using different methods and mock data The Pantheon+ Analysis: The Full Dataset and Light-Curve Release

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Cosmo-Learn: code for learning cosmology using different methods and mock data The Pantheon+ Analysis: Cosmological Constraints

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Cosmo-Learn: code for learning cosmology using different methods and mock data Evolution of the $f\sigma_8$ tension with the Planck15/$\Lambda$CDM determination and implications for modified gravity theories

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Cosmo-Learn: code for learning cosmology using different methods and mock data DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations

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Cosmo-Learn: code for learning cosmology using different methods and mock data Data analysis recipes: Using Markov Chain Monte Carlo

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Cosmo-Learn: code for learning cosmology using different methods and mock data Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

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Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmological parameter estimation with Genetic Algorithms

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This paper cites Neural Network Reconstruction of $H'(z)$ and its application in Teleparallel Gravity.

Cosmo-Learn: code for learning cosmology using different methods and mock data Neural Network Reconstruction of $H'(z)$ and its application in Teleparallel Gravity

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Cosmo-Learn: code for learning cosmology using different methods and mock data Neural Network Reconstruction of Late-Time Cosmology and Null Tests

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Cosmo-Learn: code for learning cosmology using different methods and mock data On the Robustness of the Constancy of the Supernova Absolute Magnitude: Non-parametric Reconstruction \& Bayesian approaches

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Cosmo-Learn: code for learning cosmology using different methods and mock data Performance of Non-Parametric Reconstruction Techniques in the Late-Time Universe

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Cosmo-Learn: code for learning cosmology using different methods and mock data Bayesian error propagation for neural-net based parameter inference

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Cosmo-Learn: code for learning cosmology using different methods and mock data Astropy: A Community Python Package for Astronomy

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Cosmo-Learn: code for learning cosmology using different methods and mock data The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package

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Cosmo-Learn: code for learning cosmology using different methods and mock data The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

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Cosmo-Learn: code for learning cosmology using different methods and mock data Scikit-learn: Machine Learning in Python

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Cosmo-Learn: code for learning cosmology using different methods and mock data Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

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Cosmo-Learn: code for learning cosmology using different methods and mock data PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Cosmo-Learn: code for learning cosmology using different methods and mock data Forecasting F(Q) cosmology with $\Lambda$CDM background using standard sirens

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Cosmo-Learn: code for learning cosmology using different methods and mock data Constraining f(Q) Cosmology with Standard Sirens

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Cosmo-Learn: code for learning cosmology using different methods and mock data Virtanen et al.,SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods 17 (2020) 261

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Cosmo-Learn: code for learning cosmology using different methods and mock data Foreman-Mackey,corner.py: Scatterplot matrices in python, The Journal of Open Source Software 1 (2016) 24

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Cosmo-Learn: code for learning cosmology using different methods and mock data Measuring the expansion history of the Universe with cosmic chronometers

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Cosmo-Learn: code for learning cosmology using different methods and mock data Constraints on the equation of state of dark energy and the Hubble constant from stellar ages and the CMB

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Cosmo-Learn: code for learning cosmology using different methods and mock data Constraints on the redshift dependence of the dark energy potential

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Cosmo-Learn: code for learning cosmology using different methods and mock data Constraining Cosmological Parameters Based on Relative Galaxy Ages

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Cosmo-Learn: code for learning cosmology using different methods and mock data Testing $\Lambda$CDM cosmology in a binned universe: anomalies in the deceleration parameter

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Cosmo-Learn: code for learning cosmology using different methods and mock data On the homogeneity of SnIa absolute magnitude in the Pantheon+ sample

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Cosmo-Learn: code for learning cosmology using different methods and mock data Silveira and I

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Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmological structure formation in a homogeneous dark energy background

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Cosmo-Learn: code for learning cosmology using different methods and mock data A parametrization of the growth index of matter perturbations in various Dark Energy models and observational prospects using a Euclid-like survey

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Cosmo-Learn: code for learning cosmology using different methods and mock data Accuracy of the growth index in the presence of dark energy perturbations

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Cosmo-Learn: code for learning cosmology using different methods and mock data Planck 2018 results. VI. Cosmological parameters

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Cosmo-Learn: code for learning cosmology using different methods and mock data The gravitational wave signal from massive black hole binaries and its contribution to the LISA data stream

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Cosmo-Learn: code for learning cosmology using different methods and mock data The imprint of massive black hole formation models on the LISA data stream

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Cosmo-Learn: code for learning cosmology using different methods and mock data Physics, Astrophysics and Cosmology with Gravitational Waves

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Cosmo-Learn: code for learning cosmology using different methods and mock data The stochastic gravitational wave background from turbulence and magnetic fields generated by a first-order phase transition

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Cosmo-Learn: code for learning cosmology using different methods and mock data Science with the space-based interferometer eLISA. II: Gravitational waves from cosmological phase transitions

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Cosmo-Learn: code for learning cosmology using different methods and mock data Science with the space-based interferometer eLISA. III: Probing the expansion of the Universe using gravitational wave standard sirens

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Cosmo-Learn: code for learning cosmology using different methods and mock data Laser Interferometer Space Antenna

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Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmological Backgrounds of Gravitational Waves

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Cosmo-Learn: code for learning cosmology using different methods and mock data Cosmology with the Laser Interferometer Space Antenna

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Cosmo-Learn: code for learning cosmology using different methods and mock data Using gravitational-wave standard sirens

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Cosmo-Learn: code for learning cosmology using different methods and mock data Late time cosmology with LISA: probing the cosmic expansion with massive black hole binary mergers as standard sirens

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Cosmo-Learn: code for learning cosmology using different methods and mock data Reducing the weak lensing noise for the gravitational wave Hubble diagram using the non-Gaussianity of the magnification distribution

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Cosmo-Learn: code for learning cosmology using different methods and mock data Delensing Gravitational Wave Standard Sirens with Shear and Flexion Maps

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