Deep Learning the Morphology of Dark Matter Substructure

Abstract

Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP dark matter from other well motivated models, including vortex substructure of dark matter condensates and superfluids. This is crucial in future efforts to identify the true nature of dark matter. We implement, for the first time, a classification approach to identifying dark matter substructure based on simulated strong lensing images with different substructure. Utilizing convolutional neural networks trained on sets of simulated images, we demonstrate the feasibility of deep neural networks to reliably distinguish among different types of dark matter substructure. With thousands of strong lensing images anticipated with the coming launch of LSST, we expect that supervised and unsupervised deep learning models will play a crucial role in determining the nature of dark matter.

arXiv comment10 pages

References in corpus (59)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• The Structure of Cold Dark Matter Halos
• Machine learning and the physical sciences
• Observational evidence for self-interacting cold dark matter
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 10 and 11 galaxy samples
• Dark Matter Search Results from a One Tonne$\times$Year Exposure of XENON1T
• Cold and Fuzzy Dark Matter
• Ultralight scalars as cosmological dark matter
• Results from a search for dark matter in the complete LUX exposure
• Small-Scale Challenges to the $\Lambda$CDM Paradigm
• Color superconductivity in dense quark matter
• The Structure of Dark Matter Haloes in Dwarf Galaxies
• QCD at Finite Baryon Density: Nucleon Droplets and Color Superconductivity
• Halo Formation in Warm Dark Matter Models
• Dark Matter Results From 54-Ton-Day Exposure of PandaX-II Experiment
• CFHTLenS: The Canada-France-Hawaii Telescope Lensing Survey
• Direct Detection of CDM Substructure
• Evidence for substructure in lens galaxies?
• Bose-Einstein Condensation of Dark Matter Axions
• Pairing in nuclear systems: from neutron stars to finite nuclei
• Late time Cosmological Phase Transition and Galactic Halo as Bose-liquid
• Detection of lensing substructure using ALMA observations of the dusty galaxy SDP.81
• Detection of a Dark Substructure through Gravitational Imaging
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Theory of Dark Matter Superfluidity
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• There is No Missing Satellites Problem
• Topological Defects and Structure Formation
• Angular Momentum and Vortex Formation in Bose-Einstein-Condensed Cold Dark Matter Haloes
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Linear Cosmological Structure Limits on Warm Dark Matter
• AutoLens: Automated Modeling of a Strong Lens's Light, Mass and Source
• The International Deep Planet Survey II: The frequency of directly imaged giant exoplanets with stellar mass
• Statistics of mass substructure from strong gravitational lensing: quantifying the mass fraction and mass function
• Dark Matter Superfluidity and Galactic Dynamics
• Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging
• Constraints on mediator-based dark matter and scalar dark energy models using $\sqrt{s}=13$ TeV $pp$ collision data collected by the ATLAS detector
• Gravitational lensing by cosmic strings: what we learn from the CSL-1 case
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• SHARP - IV. An apparent flux ratio anomaly resolved by the edge-on disc in B0712+472
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• The 21 cm Signature of Cosmic String Wakes
• Chiral Gravitational Waves and Baryon Superfluid Dark Matter
• On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses
• Axions and the Galactic Angular Momentum Distribution
• Gravitational Lensing and the Power Spectrum of Dark Matter Substructure: Insights from the ETHOS N-body Simulations
• Searching for Cosmic Strings in New Observational Windows
• Unified Superfluid Dark Sector
• A Dark Census: Statistically Detecting the Satellite Populations of Distant Galaxies
• Probing the small-scale structure in strongly lensed systems via transdimensional inference
• Beyond subhalos: Probing the collective effect of the Universe's small-scale structure with gravitational lensing
• Dark Disk Substructure and Superfluid Dark Matter
• Gravitationally bound BCS state as dark matter
• A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps
• Quantifying the power spectrum of small-scale structure in semi-analytic galaxies
• Direct Observation of Cosmic Strings via their Strong Gravitational Lensing Effect: II. Results from the HST/ACS Image Archive
• Direct Observation of Cosmic Strings via their Strong Gravitational Lensing Effect: I. Predictions for High Resolution Imaging Surveys
• Searching for Signatures of Cosmic String Wakes in 21cm Redshift Surveys using Minkowski Functionals
• Inferring Cosmic String Tension through the Neural Network Prediction of String Locations in CMB Maps

Cited by in corpus (24)

• Ultra-Light Dark Matter
• Wave Dark Matter
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• Vortices and waves in light dark matter
• Implications of Milky Way Substructures for the Nature of Dark Matter
• Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
• Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses
• Convolutional Neural Networks for Direct Detection of Dark Matter
• Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
• Image segmentation for analyzing galaxy-galaxy strong lensing systems
• Strongly-Interacting Ultralight Millicharged Particles
• Astrophysical Tests of Dark Matter Self-Interactions
• One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses
• Cosmic Filament Spin from Dark Matter Vortices
• Charting Galactic Accelerations with Stellar Streams and Machine Learning
• Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks
• Deep learning the astrometric signature of dark matter substructure
• Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
• Locating Hidden Exoplanets in ALMA Data Using Machine Learning
• A machine learning-based methodology for pulse classification in dual-phase xenon time projection chambers
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning