Massive $\nu$s through the CNN lens: interpreting the field-level neutrino mass information in weak lensing

Abstract

Modern cosmological surveys probe the Universe deep into the nonlinear regime, where massive neutrinos suppress cosmic structure. Traditional cosmological analyses, which use the 2-point correlation function to extract information, are no longer optimal in the nonlinear regime, and there is thus much interest in extracting beyond-2-point information to improve constraints on neutrino mass. Quantifying and interpreting the beyond-2-point information is thus a pressing task. We study the field-level information in weak lensing convergence maps using convolution neural networks. We find that the network performance increases as higher source redshifts and smaller scales are considered -- investigating up to a source redshift of 2.5 and ${\ell }_{\max }\simeq 10^4$ -- verifying that massive neutrinos leave a distinct effect on weak lensing. However, the performance of the network significantly drops after scaling out the 2-point information from the maps, implying that most of the field-level information can be found in the 2-point correlation function alone. We quantify these findings in terms of the likelihood ratio and also use Integrated Gradient saliency maps to interpret which parts of the map the network is learning the most from. We find that, in the absence of noise, the network extracts a similar amount of information from the most overdense and underdense regions. However, upon adding noise, the information in underdense regions is distorted as noise disproportionately washes out void-like structures.

arXiv comment10+5 pages, 5 figures, minor changes to reflect final version accepted by JCAP

References in corpus (73)

• The cosmological simulation code GADGET-2
• Evidence for oscillation of atmospheric neutrinos
• Direct Evidence for Neutrino Flavor Transformation from Neutral-Current Interactions in the Sudbury Neutrino Observatory
• Axiomatic Attribution for Deep Networks
• Observation of electron-antineutrino disappearance at Daya Bay
• The Simons Observatory: Science goals and forecasts
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Measurement of Neutrino Oscillation with KamLAND: Evidence of Spectral Distortion
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• Measurement of Neutrino Oscillation by the K2K Experiment
• Captum: A unified and generic model interpretability library for PyTorch
• An improved upper limit on the neutrino mass from a direct kinematic method by KATRIN
• The Neutrino Puzzle: Anomalies, Interactions, and Cosmological Tensions
• Bayesian physical reconstruction of initial conditions from large scale structure surveys
• Physical Bayesian modelling of the non-linear matter distribution: new insights into the Nearby Universe
• Cosmological constraints with deep learning from KiDS-450 weak lensing maps
• Prime Focus Spectrograph (PFS) for the Subaru Telescope: Overview, recent progress, and future perspectives
• Constraining $M_{\nu }$ with the Bispectrum I: Breaking Parameter Degeneracies
• Massive Neutrinos Leave Fingerprints on Cosmic Voids
• Non-Gaussian information from weak lensing data via deep learning
• MassiveNuS: Cosmological Massive Neutrino Simulations
• Likelihood-free inference with neural compression of DES SV weak lensing map statistics
• Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform
• Fisher for complements: Extracting cosmology and neutrino mass from the counts-in-cells PDF
• Constraining $M_{\nu }$ with the Bispectrum II: The Total Information Content of the Galaxy Bispectrum
• Using the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure
• Towards optimal extraction of cosmological information from nonlinear data
• Weak lensing cosmology with convolutional neural networks on noisy data
• The imprint of neutrinos on clustering in redshift-space
• Detecting Neutrino Mass by Combining Matter Clustering, Halos, and Voids
• Cosmological constraints from noisy convergence maps through deep learning
• Constraining neutrino mass with tomographic weak lensing one-point probability distribution function and power spectrum
• The GIGANTES dataset: precision cosmology from voids in the machine learning era
• Nuw CDM cosmology from the weak lensing convergence PDF
• An Efficient and Accurate Hybrid Method for Simulating Non-Linear Neutrino Structure
• Machine learning cosmological structure formation
• Constraining Neutrino Mass with the Tomographic Weak Lensing Bispectrum
• Constraining neutrino mass with weak lensing Minkowski Functionals
• Constraining neutrino mass with tomographic weak lensing peak counts
• Weak lensing scattering transform: dark energy and neutrino mass sensitivity
• Reducing Noise in Cosmological N-body Simulations with Neutrinos
• An $n$-th order Lagrangian Forward Model for Large-Scale Structure
• Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
• FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver
• Interpreting deep learning models for weak lensing
• Science Impacts of the SPHEREx All-Sky Optical to Near-Infrared Spectral Survey II: Report of a Community Workshop on the Scientific Synergies Between the SPHEREx Survey and Other Astronomy Observatories
• Bayesian evidence for the tensor-to-scalar ratio $r$ and neutrino masses $m_{\nu }$: Effects of uniform vs logarithmic priors
• Weak Lensing Minima and Peaks: Cosmological Constraints and the Impact of Baryons
• An interpretable machine learning framework for dark matter halo formation
• A fast particle-mesh simulation of non-linear cosmological structure formation with massive neutrinos
• Aemulus $\nu$: Precise Predictions for Matter and Biased Tracer Power Spectra in the Presence of Neutrinos
• Bayesian forward modelling of cosmic shear data
• The MillenniumTNG Project: The impact of baryons and massive neutrinos on high-resolution weak gravitational lensing convergence maps
• Beware of Fake $\nu$s: The Effect of Massive Neutrinos on the Nonlinear Evolution of Cosmic Structure
• Cosmological constraints from HSC survey first-year data using deep learning
• A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
• The look-elsewhere effect from a unified Bayesian and frequentist perspective
• Explaining deep learning of galaxy morphology with saliency mapping
• HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows
• Starlet l1-norm for weak lensing cosmology
• Joint velocity and density reconstruction of the Universe with nonlinear differentiable forward modeling
• Neural networks as optimal estimators to marginalize over baryonic effects
• A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks
• Explaining dark matter halo density profiles with neural networks
• Field-level inference of cosmic shear with intrinsic alignments and baryons
• pmwd: A Differentiable Cosmological Particle-Mesh $N$-body Library
• Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
• Deep learning insights into cosmological structure formation
• Field-Level Inference with Microcanonical Langevin Monte Carlo
• Improving initialization and evolution accuracy of cosmological neutrino simulations
• The HalfDome Multi-Survey Cosmological Simulations: N-body Simulations
• Significance of void shape: Neutrino mass from Voronoi void halos?
• Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference

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