georgestein / ml-in-cosmology

A comprehensive list of published machine learning applications to cosmology

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ml-in-cosmology

This github attempts to maintain a comprehensive list of published machine learning applications to cosmology, organized by subject matter and arxiv posting date. I've also included some more obsevational galaxy studies.

Each entry contains the paper title, a simple summary of the machine learning methods used in the work, and the arxiv link. If I have missed any papers that you believe should be included please email me at george.f.stein@gmail.com or issue a pull request.

For general reviews of the subject, or for public machine-learning ready datasets, see the resources at the bottom of this list.

Feel free to cite in any works DOI

I am currently a postdoctoral researcher at the Berkeley Center for Cosmological Physics and Lawrence Berkeley National Laboratory, broadly working on machine learning in cosmology. Cheers to whoever can find which of the papers below have me as a (co-)author 🍻


Table of Contents

Dictionary

A dictionary of all abbreviations for machine learning methods used in this compilation. In general I adopted those used by the authors, except in a few cases. The links are to explanatory articles that I personally like.

 


Large-Scale Structure

The Large-Scale Structure of the universe is a field that largely relies on state-of-the art cosmological simulations. Due to the computational complexity of these simulations, some investigations will remain computationally-infeasible for the forseeable future, and machine learning techniques can have a number of important uses.

Structure Formation

Title ML technique(s) used arxiv link
A First Look at creating mock catalogs with machine learning techniques SVM, kNN https://arxiv.org/abs/1303.1055
Machine Learning Etudes in Astrophysics: Selection Functions for Mock Cluster Catalogs SVM, GMM https://arxiv.org/abs/1409.1576
PkANN I&2. Non-linear matter power spectrum interpolation through artificial neural networks NN https://arxiv.org/abs/1203.1695, https://arxiv.org/abs/1312.2101
Machine learning and cosmological simulations I.&II. kNN, DT, RF, EXT https://arxiv.org/abs/1510.06402 https://arxiv.org/abs/1510.07659
Estimating Cosmological Parameters from the Dark Matter Distribution CNN https://arxiv.org/abs/1711.02033
Painting galaxies into dark matter haloes using machine learning SVR, kNN, MLP, DT, RF, EXT, AdR https://arxiv.org/abs/1712.03255
Modeling the Impact of Baryons on Subhalo Populations with Machine Learning RF https://arxiv.org/abs/1712.04467
Fast cosmic web simulations with generative adversarial networks GAN https://arxiv.org/abs/1801.09070
Machine learning cosmological structure formation RF https://arxiv.org/abs/1802.04271
A Machine Learning Approach to Galaxy-LSS Classification I: Imprints on Halo Merger Trees SVM https://arxiv.org/abs/1803.11156
Classifying the Large Scale Structure of the Universe with Deep Neural Networks V-Net https://arxiv.org/abs/1804.00816
Cosmological Reconstruction From Galaxy Light: Neural Network Based Light-Matter Connection NN https://arxiv.org/abs/1805.02247
A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues V-Net https://arxiv.org/abs/1805.04537
Learning to Predict the Cosmological Structure Formation V-Net https://arxiv.org/abs/1811.06533
deepCool: Fast and Accurate Estimation of Cooling Rates in Irradiated Gas with Artificial Neural Networks NN, RF, kNN https://arxiv.org/abs/1901.01264
From Dark Matter to Galaxies with Convolutional Networks V-Net https://arxiv.org/abs/1902.05965
Painting halos from 3D dark matter fields using Wasserstein mapping networks GAN https://arxiv.org/abs/1903.10524
Painting with baryons: augmenting N-body simulations with gas using deep generative models GAN, VAE https://arxiv.org/abs/1903.12173
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks GAN https://arxiv.org/abs/1904.12846
A deep learning model to emulate simulations of cosmic reionization CNN https://arxiv.org/abs/1905.06958
An interpretable machine learning framework for dark matter halo formation BDT https://arxiv.org/abs/1906.06339
Cosmological N-body simulations: a challenge for scalable generative models GAN https://arxiv.org/abs/1908.05519
Cosmological parameter estimation from large-scale structure deep learning CNN https://arxiv.org/abs/1908.10590
Neural physical engines for inferring the halo mass distribution function NPE https://arxiv.org/abs/1909.06379
A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys CNN https://arxiv.org/abs/1909.10527
A black box for dark sector physics: Predicting dark matter annihilation feedback with conditional GANs cGAN https://arxiv.org/abs/1910.00291
Learning neutrino effects in Cosmology with Convolutional Neural Networks V-Net https://arxiv.org/abs/1910.04255
Predicting dark matter halo formation in N-body simulations with deep regression networks V-Net https://arxiv.org/abs/1912.04299
Probabilistic cosmic web classification using fast-generated training data RF https://arxiv.org/abs/1912.04412
Super-resolution emulator of cosmological simulations using deep physical models WGAN https://arxiv.org/abs/2001.05519
Baryon acoustic oscillations reconstruction using convolutional neural networks CNN https://arxiv.org/abs/2002.10218
Emulation of cosmological mass maps with conditional generative adversarial networks GAN https://arxiv.org/abs/2004.08139
Towards Universal Cosmological Emulators with Generative Adversarial Networks GAN https://arxiv.org/abs/2004.10223
Nonlinear 3D Cosmic Web Simulation with Heavy-Tailed Generative Adversarial Networks GAN https://arxiv.org/abs/2005.03050
GalaxyNet: Connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes RF, NN https://arxiv.org/abs/2005.12276
Discovering Symbolic Models from Deep Learning with Inductive Biases GNN https://arxiv.org/abs/2006.11287
Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps V-Net https://arxiv.org/abs/2007.07267
HInet: Generating neutral hydrogen from dark matter with neural networks CNN https://arxiv.org/abs/2007.10340
Machine Learning the Fates of Dark Matter Subhalos: A Fuzzy Crystal Ball RF, BDT https://arxiv.org/abs/2008.05001
Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning LDL https://arxiv.org/abs/2010.02926
AI-assisted super-resolution cosmological simulations GAN https://arxiv.org/abs/2010.06608
Encoding large scale cosmological structure with Generative Adversarial Networks GAN https://arxiv.org/abs/2011.05244
Deep learning insights into cosmological structure formation CNN https://arxiv.org/abs/2011.10577
SHAPing the Gas: Understanding Gas Shapes in Dark Matter Haloes with Interpretable Machine Learning XGBoost https://arxiv.org/abs/2011.12987
The BACCO Simulation Project: A baryonification emulator with Neural Networks NN https://arxiv.org/abs/2011.15018
dm2gal: Mapping Dark Matter to Galaxies with Neural Networks CNN https://arxiv.org/abs/2012.00186
Fast and Accurate Non-Linear Predictions of Universes with Deep Learning V-Net https://arxiv.org/abs/2012.00240
Identifying Cosmological Information in a Deep Neural Network CNN https://arxiv.org/abs/2012.03778
CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines RIM https://arxiv.org/abs/2104.12864
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics GAN https://arxiv.org/abs/2105.01016
Cosmic Velocity Field Reconstruction Using AI V-Net https://arxiv.org/abs/2105.09450
Normalizing flows for random fields in cosmology NF https://arxiv.org/abs/2105.12024
Cosmic Voids in GAN-Generated Maps of Large-Scale Structure GAN https://arxiv.org/abs/2106.04014
Classification algorithms applied to structure formation simulations RF https://arxiv.org/abs/2106.06587
Fast, high-fidelity Lyman α forests with convolutional neural networks V-Net https://arxiv.org/abs/2106.12662
HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics VAE https://arxiv.org/abs/2106.12675
Predicting halo occupation and galaxy assembly bias with machine learning RF https://arxiv.org/abs/2107.01223
Finding universal relations in subhalo properties with artificial intelligence NN https://arxiv.org/abs/2109.04484
Multifield Cosmology with Artificial Intelligence CNN https://arxiv.org/abs/2109.09747
Robust marginalization of baryonic effects for cosmological inference at the field level CNN https://arxiv.org/abs/2109.10360
Inpainting hydrodynamical maps with deep learning CNN https://arxiv.org/abs/2109.07070
Deep learning reconstruction of three-dimensional galaxy distributions with intensity mapping observations CNN https://arxiv.org/abs/2110.05755
From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with Deep Learning GAN https://arxiv.org/abs/2110.11970
NECOLA: Towards a Universal Field-level Cosmological Emulator V-Net https://arxiv.org/abs/2111.02441
Modeling the galaxy-halo connection with machine learning RF https://arxiv.org/abs/2111.02422
Super-resolving Dark Matter Halos using Generative Deep Learning U-Net, GAN https://arxiv.org/abs/2111.06393
Inferring halo masses with Graph Neural Networks GNN https://arxiv.org/abs/2111.08683
How to quantify fields or textures? A guide to the scattering transform ST https://arxiv.org/abs/2112.01288
Neural Network Acceleration of Large-scale Structure Theory Calculations NN https://arxiv.org/abs/2112.05889
Physical Benchmarking for AI-Generated Cosmic Web U-Net https://arxiv.org/abs/2112.05681
Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy Formation ERT https://arxiv.org/abs/2112.08424
Unravelling the role of cosmic velocity field in dark matter halo mass function using deep learning CNN https://arxiv.org/abs/2112.14743
Cosmology with one galaxy? NN, GBT https://arxiv.org/abs/2201.02202
Mimicking the halo-galaxy connection using machine learning DT, kNN, GBM, NN https://arxiv.org/abs/2201.06054
The scatter in the galaxy-halo connection: a machine learning analysis NN https://arxiv.org/abs/2202.14006
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks CNN https://arxiv.org/abs/2203.00026
Discovering the building blocks of dark matter halo density profiles with neural networks CNN https://arxiv.org/abs/2203.08827
A Machine Learning Approach to Correct for Mass Resolution Effects in Simulated Halo Clustering Statistics RF https://arxiv.org/abs/2203.12669
Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite NN https://arxiv.org/abs/2204.02408
Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment GNN https://arxiv.org/abs/2204.07077
Learning cosmology and clustering with cosmic graphs GNN https://arxiv.org/abs/2204.13713
Insights into the origin of halo mass profiles from machine learning GB https://arxiv.org/abs/2205.04474
Fast and realistic large-scale structure from machine-learning-augmented random field simulations U-Net https://arxiv.org/abs/2205.07898
Field Level Neural Network Emulator for Cosmological N-body Simulations V-Net https://arxiv.org/abs/2206.04594
Reconstructing the Universe with Variational self-Boosted Sampling HMC, NF https://arxiv.org/abs//2206.15433
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues GNN https://arxiv.org/abs/2207.05202
Hybrid Physical-Neural ODEs for Fast N-body Simulations NN https://arxiv.org/abs/2207.05509
CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks NN https://arxiv.org/abs/2207.05707
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks BNN https://arxiv.org/abs/2207.08435

Structure Identification

Title ML technique(s) used arxiv link
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters SDM https://arxiv.org/abs/1410.0686, https://arxiv.org/abs/1509.05409
A Deep Learning Approach to Galaxy Cluster X-ray Masses CNN https://arxiv.org/abs/1810.07703
An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations OLR, RR, BRR, KRR, SVR, DT, BDT, ADA, kNN https://arxiv.org/abs/1810.08430
Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach XGBoost, RF, NN https://arxiv.org/abs/1902.02680
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters CNN https://arxiv.org/abs/1902.05950
Multiwavelength cluster mass estimates and machine learning GB, RF https://arxiv.org/abs/1905.09920
Self-supervised Learning with Physics-aware Neural Networks I: Galaxy Model Fitting AE https://arxiv.org/abs/1907.03957
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning RF https://arxiv.org/abs/1908.02765
Large-scale structures in the LCDM Universe: network analysis and machine learning XGBoost https://arxiv.org/abs/1910.07868
Dynamical mass inference of galaxy clusters with neural flows NF (MADE) https://arxiv.org/abs/2003.05951
Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effect U-Net https://arxiv.org/abs/2003.06135
Galaxy cluster mass estimation with deep learning and hydrodynamical simulations CNN https://arxiv.org/abs/2005.11819
Mass Estimation of Galaxy Clusters with Deep Learning II: CMB Cluster Lensing U-NET https://arxiv.org/abs/2005.13985
Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks CNN https://arxiv.org/abs/2006.05998
Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data AE, RF https://arxiv.org/abs/2006.08235
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters BNN https://arxiv.org/abs/2006.13231
A deep learning view of the census of galaxy clusters in IllustrisTNG CNN https://arxiv.org/abs/2007.05144
Revealing the Local Cosmic Web by Deep Learning V-Net https://arxiv.org/abs/2008.01738
Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks CNN https://arxiv.org/abs/2009.03340
Weak-lensing Mass Reconstruction of Galaxy Clusters with Convolutional Neural Network CNN https://arxiv.org/abs/2102.05403
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning CNN https://arxiv.org/abs/2102.13123
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains CNN https://arxiv.org/abs/2103.01373
A novel Cosmic Filament catalogue from SDSS data XGBoost https://arxiv.org/abs/2106.05253
Mass Estimation of Planck Galaxy Clusters using Deep Learning CNN https://arxiv.org/abs/2111.01933
Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning CNN https://arxiv.org/abs/2203.06288
Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations RF, XGBoost, NN https://arxiv.org/abs/2204.10751
Revealing the Galaxy-Halo Connection Through Machine Learning GB https://arxiv.org/abs/2204.10332
A deep learning approach to halo merger tree construction GAN https://arxiv.org/abs/2205.15988
Towards reconstructing the halo clustering and halo mass function of N-body simulations using neural ratio estimation CNN https://arxiv.org/abs/2206.11312
Classification of cosmic structures for galaxies with deep learning: connecting cosmological simulations with observations CNN https://arxiv.org/abs/2207.09072

 


Reionization and 21cm

In cosmology, the process of Reionization refers to the period when our universe went from the "Dark Ages" - before major star and galaxy formation - to the ionized state we see today.

Title ML technique(s) used arxiv link
A machine-learning approach to measuring the escape of ionizing radiation from galaxies in the reionization epoch LR https://arxiv.org/abs/1603.09610
Analysing the 21 cm signal from the epoch of reionization with artificial neural networks NN https://arxiv.org/abs/1701.07026
Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks NN https://arxiv.org/abs/1708.00011
Reionization Models Classifier using 21cm Map Deep Learning CNN https://arxiv.org/abs/1801.06381
Deep learning from 21-cm images of the Cosmic Dawn CNN https://arxiv.org/abs/1805.02699
Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks CNN https://arxiv.org/abs/1807.03317
Evaluating machine learning techniques for predicting power spectra from reionization simulations SVM, MLP, GPR https://arxiv.org/abs/1811.09141
Improved supervised learning methods for EoR parameters reconstruction CNN https://arxiv.org/abs/1904.04106
Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA CNN https://arxiv.org/abs/1907.07787
Emulating the Global 21-cm Signal from Cosmic Dawn and Reionization NN https://arxiv.org/abs/1910.06274
21cm Global Signal Extraction: Extracting the 21cm Global Signal using Artificial Neural Networks NN https://arxiv.org/abs/1911.02580
A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs GAN https://arxiv.org/abs/2002.07940
Beyond the power spectrum - I: recovering H II bubble size distribution from 21 cm power spectrum with artificial neural networks NN https://arxiv.org/abs/2002.08238
Foreground modelling via Gaussian process regression: an application to HERA data GP https://arxiv.org/abs/2004.06041
Predicting 21cm-line map from Lyman α emitter distribution with Generative Adversarial Networks GAN https://arxiv.org/abs/2004.09206
Constraining the Reionization History using Bayesian Normalizing Flows NF https://arxiv.org/abs/2005.07694
Deep-Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization CNN https://arxiv.org/abs/2006.06236
Removing Astrophysics in 21 cm maps with Neural Networks CNN https://arxiv.org/abs/2006.14305
Deep Forest: Neural Network reconstruction of the Lyman-alpha forest NN https://arxiv.org/abs/2009.10673
deep21: a Deep Learning Method for 21cm Foreground Removal U-Net https://arxiv.org/abs/2010.15843
Analysing the Epoch of Reionization with three-point correlation functions and machine learning techniques NN https://arxiv.org/abs/2011.14157
Using Artificial Neural Networks to extract the 21-cm Global Signal from the EDGES data NN https://arxiv.org/abs/2012.00028
Modeling assembly bias with machine learning and symbolic regression RF, SR https://arxiv.org/abs/2012.00111
Reconstructing Patchy Reionization with Deep Learning U-Net https://arxiv.org/abs/2101.01214
Deep learning approach for identification of HII regions during reionization in 21-cm observations U-Net https://arxiv.org/abs/2102.06713
GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation NN https://arxiv.org/abs/2104.04336
Machine learning galaxy properties from 21 cm lightcones: impact of network architectures and signal contamination CNN https://arxiv.org/abs/2107.00018
21cmVAE: A VAE-based Emulator of the 21-cm Global Signal VAE https://arxiv.org/abs/2107.05581
Improving constraints on the reionization parameters using 21-cm bispectrum NN https://arxiv.org/abs/2108.07279
Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks CNN https://arxiv.org/abs/2108.07972
Deep Forest: Neural Network reconstruction of the Lyman-alpha forest NN https://arxiv.org/abs/2009.10673
HIFlow: Generating Diverse HI Maps Conditioned on Cosmology using Normalizing Flow NF https://arxiv.org/abs/2110.02983
Understanding the Impact of Semi-Numeric Reionization Models when using CNNs CNN https://arxiv.org/abs/2112.03443
Extracting the 21-cm Power Spectrum and the reionization parameters from mock datasets using Artificial Neural Networks NN https://arxiv.org/abs/2112.13866
Inferring Astrophysics and Dark Matter Properties from 21cm Tomography using Deep Learning CNN https://arxiv.org/abs/2201.07587
Machine Learning to Decipher the Astrophysical Processes at Cosmic Dawn NN https://arxiv.org/abs/2201.08205
Estimation of HII Bubble Size Distribution from 21cm Power Spectrum with Artificial Neural Networks NN https://arxiv.org/abs/2002.08238
Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning V-Net https://arxiv.org/abs/2204.02780

 

Gravitational Lensing

Gravitational lensing in cosmology refers to the bending of light due to mass between the source and Earth. This effect is very useful for inferring properties of the total mass distribution in our Universe, which is dominated by dark matter that we cannot see electromagnetically. Gravitational lensing comes in two types: weak and strong.

Strong gravitational lensing refers to the cases where the lensing effect is strong enough to be seen by the human eye, or equivalent, on an astronomical image. For example, we might see multiple images of a background galaxy, or clear shape distortions. This only happens when a massive galaxy cluster lies between us and some background galaxies

Weak gravitational lensing refers to the global effect that almost all far away galaxies are gravitationally lensed by a small amount, which changes their observed shape by roughly 1%. This can only be measured statistically when given a large number of samples, and not on an object-to-object basis.

Weak Lensing

Title ML technique(s) used arxiv link
Bias-Free Shear Estimation using Artificial Neural Networks NN https://arxiv.org/abs/1002.0838
Hopfield Neural Network deconvolution for weak lensing measurement HNN https://arxiv.org/abs/1411.3193
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks GAN https://arxiv.org/abs/1706.02390
Cosmological model discrimination with Deep Learning CNN https://arxiv.org/abs/1707.05167
Non-Gaussian information from weak lensing data via deep learning CNN https://arxiv.org/abs/1802.01212
Learning from deep learning: better cosmological parameter inference from weak lensing maps CNN https://arxiv.org/abs/1806.05995
Weak-lensing shear measurement with machine learning: teaching artificial neural networks about feature noise NN https://arxiv.org/abs/1807.02120
Cosmological constraints from noisy convergence maps through deep learning CNN https://arxiv.org/abs/1807.08732
Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach CNN https://arxiv.org/abs/1808.07491
On the dissection of degenerate cosmologies with machine learning CNN https://arxiv.org/abs/1810.11027
Distinguishing standard and modified gravity cosmologies with machine learning CNN https://arxiv.org/abs/1810.11030
Denoising Weak Lensing Mass Maps with Deep Learning GAN https://arxiv.org/abs/1812.05781
Weak lensing cosmology with convolutional neural networks on noisy data CNN https://arxiv.org/abs/1902.03663
Galaxy shape measurement with convolutional neural networks CNN https://arxiv.org/abs/1902.08161
Cosmological constraints with deep learning from KiDS-450 weak lensing maps CNN https://arxiv.org/abs/1906.03156
Deep learning dark matter map reconstructions from DES SV weak lensing data U-Net https://arxiv.org/abs/1908.00543
Decoding Cosmological Information in Weak-Lensing Mass Maps with Generative Adversarial Networks GAN https://arxiv.org/abs/1911.12890
Parameter Inference for Weak Lensing using Gaussian Processes and MOPED GP https://arxiv.org/abs/2005.06551
Shear measurement bias II: a fast machine learning calibration method MLP https://arxiv.org/abs/2006.07011
Interpreting deep learning models for weak lensing CNN https://arxiv.org/abs/2007.06529
Probabilistic Mapping of Dark Matter by Neural Score Matching DE https://arxiv.org/abs/2011.08271
Higher order statistics of shear field: a machine learning approach kNN, SVM, GP, RF, etc.. https://arxiv.org/abs/2011.10438
Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing CNN https://arxiv.org/abs/2109.11060
Probabilistic Mass Mapping with Neural Score Estimation NSM https://arxiv.org/abs/2201.05561
Accelerating cosmological inference with Gaussian processes and neural networks -- an application to LSST Y1 weak lensing and galaxy clustering NN, GP https://arxiv.org/abs/2203.06124

Strong Lensing

Title ML technique(s) used arxiv link
A neural network gravitational arc finder based on the Mediatrix filamentation method NN https://arxiv.org/abs/1607.04644
CMU DeepLens: deep learning for automatic image-based galaxy-galaxy strong lens finding CNN https://arxiv.org/abs/1703.02642
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique HoG https://arxiv.org/abs/1704.02322
Finding strong lenses in CFHTLS using convolutional neural networks CNN https://arxiv.org/abs/1704.02744
Fast automated analysis of strong gravitational lenses with convolutional neural networks CNN https://arxiv.org/abs/1708.08842
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing NN https://arxiv.org/abs/1708.08843
The Strong Gravitational Lens Finding Challenge SVM, CNN https://arxiv.org/abs/1802.03609
Testing convolutional neural networks for finding strong gravitational lenses in KiDS CNN https://arxiv.org/abs/1807.04764
Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks RNN, CNN https://arxiv.org/abs/1808.00011
Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines RIM, CNN https://arxiv.org/abs/1901.01359
Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey CNN https://arxiv.org/abs/1906.00970
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning NN https://arxiv.org/abs/1909.02005
Deep Learning the Morphology of Dark Matter Substructure CNN https://arxiv.org/abs/1909.07346
Circumventing Lens Modeling to Detect Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks CNN https://arxiv.org/abs/1910.00015
Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming VAE https://arxiv.org/abs/1910.06157
Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder VAE https://arxiv.org/abs/1911.04320
HOLISMOKES II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks CNN https://arxiv.org/abs/2004.13048
Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys CNN https://arxiv.org/abs/2005.04730
Dark Matter Subhalos, Strong Lensing and Machine Learning CNN https://arxiv.org/abs/2005.05353
Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3 CNN https://arxiv.org/abs/2007.00188
Decoding Dark Matter Substructure without Supervision AE, VAE, AAE https://arxiv.org/abs/2008.12731
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation U-Net https://arxiv.org/abs/2009.06639
Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation U-Net https://arxiv.org/abs/2009.06663
Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks CNN https://arxiv.org/abs/2010.12960
Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies GP, ... https://arxiv.org/abs/2010.07032
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant BNN https://arxiv.org/abs/2012.00042
Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks CNN https://arxiv.org/abs/2109.00014
Finding quadruply imaged quasars with machine learning. I. Methods CNN, VAE https://arxiv.org/abs/2109.09781
Mining for strong gravitational lenses with self-supervised learning SSL https://arxiv.org/abs/2110.00023
High-quality strong lens candidates in the final Kilo Degree survey footprint CNN https://arxiv.org/abs/2110.01905
The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint CNN https://arxiv.org/abs/2110.02418
Finding Strong Gravitational Lenses Through Self-Attention CNN, ViT https://arxiv.org/abs/2110.09202
A search for galaxy-scale strong gravitational lenses in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) CNN https://arxiv.org/abs/2110.11972
Exploring the interpretability of deep neural networks used for gravitational lens finding with a sensitivity probe CNN https://arxiv.org/abs/2112.02479
Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations CNN https://arxiv.org/abs/2202.12776
GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling HMC https://arxiv.org/abs/2202.07663
Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge CNN https://arxiv.org/abs/2203.09536
DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning CNN https://arxiv.org/abs/2204.05924
New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9 CNN https://arxiv.org/abs/2206.02764
Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation CNN https://arxiv.org/abs/2205.09126
HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images CNN https://arxiv.org/abs/2206.11279
Strong Lensing Source Reconstruction Using Continuous Neural Fields NF https://arxiv.org/abs/2206.14820
Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machines VAE, RIM https://arxiv.org/abs/2207.01073
Population-Level Inference of Strong Gravitational Lenses with Neural Network-Based Selection Correction CNN https://arxiv.org/abs/2207.04123
HOLISMOKES - X. Comparison between neural network and semi-automated traditional modeling of strong lenses NN https://arxiv.org/abs/2207.10124
A machine learning based approach to gravitational lens identification with the International LOFAR Telescope CNN https://arxiv.org/abs/2207.10698

 


Cosmic Microwave Background

The Cosmic Microwave Background (CMB) is the light left over from the period of recombination in the very early Universe, 380,000 years after the beginning. CMB observations are sometimes referred to as "baby pictures of our Universe", as this light has been travelling for 13.5 billion years just to reach us. As this light travels towards us it can be effected by the large scale structure along the line of sight, which can be used as a probe of structure formation.

Title ML technique(s) used arxiv link
DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks CNN https://arxiv.org/abs/1810.01483
Fast Wiener filtering of CMB maps with Neural Networks U-Net https://arxiv.org/abs/1905.05846
CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning GAN https://arxiv.org/abs/1908.04682
CosmoVAE: Variational Autoencoder for CMB Image Inpainting VAE https://arxiv.org/abs/2001.11651
Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network GAN https://arxiv.org/abs/2003.13691
Inpainting via Generative Adversarial Networks for CMB data analysis GAN https://arxiv.org/abs/2004.04177
Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning BNN https://arxiv.org/abs/2004.11507
Foreground model recognition through Neural Networks for CMB B-mode observations NN https://arxiv.org/abs/2003.02278
Inpainting CMB maps using Partial Convolutional Neural Networks U-Net https://arxiv.org/abs/2011.01433
ForSE: a GAN based algorithm for extending CMB foreground models to sub-degree angular scales GAN https://arxiv.org/abs/2011.02221
A Generative Model of Galactic Dust Emission Using Variational Inference VAE https://arxiv.org/abs/2101.11181
A convolutional-neural-network estimator of CMB constraints on dark matter energy injection CNN https://arxiv.org/abs/2101.10360
An Unbiased Estimator of the Full-sky CMB Angular Power Spectrum using Neural Networks NN https://arxiv.org/abs/2102.04327
MillimeterDL: Deep Learning Simulations of the Microwave Sky U-Net https://arxiv.org/abs/2105.11444
Reconstructing Cosmic Polarization Rotation with ResUNet-CMB U-Net https://arxiv.org/abs/2109.09715
Single frequency CMB B-mode inference with realistic foregrounds from a single training image U-Net https://arxiv.org/abs/2111.01138
Convolutional Neural Network-reconstructed velocity for kinetic SZ detection CNN https://arxiv.org/abs/2201.01643
Cosmic Kite: Auto-encoding the Cosmic Microwave Background AE https://arxiv.org/abs/2202.05853
Estimation of Full Sky CMB Angular Power Spectrum from High Resolution Partial Sky CMB Map Using Artificial Neural Network NN https://arxiv.org/abs/2203.14060
Application of Genetic Algorithm to Estimate the Large Angular Scale Features of Cosmic Microwave Background GA https://arxiv.org/abs/2102.06569
Recovering the CMB Signal with Machine Learning CNN https://arxiv.org/abs/2204.01820
CENN: a fully convolutional neural network for CMB recovery in realistic microwave sky simulations U-Net https://arxiv.org/abs/2205.05623
High-Resolution CMB Lensing Reconstruction with Deep Learning U-Net https://arxiv.org/abs/2205.07368

 


Observational

This section has a variety of machine learning papers used for various observational applications, mainly focusing on redshift estimation and various galaxy classification problems.

Redshifts

Title ML technique(s) used arxiv link
ANNz: estimating photometric redshifts using artificial neural networks NN https://arxiv.org/abs/astro-ph/0311058
Estimating Photometric Redshifts Using Support Vector Machines SVM https://arxiv.org/abs/astro-ph/0412005
Robust Machine Learning Applied to Astronomical Data Sets. II. Quantifying Photometric Redshifts for Quasars Using Instance-based Learning kNN https://arxiv.org/abs/astro-ph/0612471
Robust Machine Learning Applied to Astronomical Data Sets. III. Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX kNN https://arxiv.org/abs/0804.3413
ArborZ: Photometric Redshifts Using Boosted Decision Trees BDT https://arxiv.org/abs/0908.4085
Unsupervised self-organised mapping: a versatile empirical tool for object selection, classification and redshift estimation in large surveys SOM https://arxiv.org/abs/1110.0005
Can Self-Organizing Maps accurately predict photometric redshifts? SOM https://arxiv.org/abs/1201.1098
TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests RF https://arxiv.org/abs/1303.7269
Estimating Photometric Redshifts of Quasars via K-nearest Neighbor Approach Based on Large Survey Databases kNN https://arxiv.org/abs/1305.5023
An approach to the analysis of SDSS spectroscopic outliers based on Self-Organizing Maps SOM https://arxiv.org/abs/1309.2418
Using neural networks to estimate redshift distributions. An application to CFHTLenS NN https://arxiv.org/abs/1312.1287
SOMz: photometric redshift PDFs with self organizing maps and random atlas SOM https://arxiv.org/abs/1312.5753
Feature importance for machine learning redshifts applied to SDSS galaxies NN, ADA https://arxiv.org/abs/1410.4696
GAz: A Genetic Algorithm for Photometric Redshift Estimation GA https://arxiv.org/abs/1412.5997
Anomaly detection for machine learning redshifts applied to SDSS galaxies ADA, SOM, BDT https://arxiv.org/abs/1503.08214
Measuring photometric redshifts using galaxy images and Deep Neural Networks CNN, ADA https://arxiv.org/abs/1504.07255
A Sparse Gaussian Process Framework for Photometric Redshift Estimation NN, GPR https://arxiv.org/abs/1505.05489
ANNz2 - photometric redshift and probability distribution function estimation using machine learning NN, BDT https://arxiv.org/abs/1507.00490
DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting kNN https://arxiv.org/abs/1511.07623
Photometric Redshift Estimation for Quasars by Integration of KNN and SVM kNN, SVM https://arxiv.org/abs/1601.01739
Stacking for machine learning redshifts applied to SDSS galaxies SOM, DT https://arxiv.org/abs/1602.06294
GPz: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts GPR https://arxiv.org/abs/1604.03593
Photo-z with CuBANz: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network NN https://arxiv.org/abs/1609.03568
Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts RF, MDN, DCMDN https://arxiv.org/abs/1706.02467
Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks NN, BDT https://arxiv.org/abs/1709.04205
Estimating Photometric Redshifts for X-ray sources in the X-ATLAS field, using machine-learning techniques RF https://arxiv.org/abs/1710.01313
Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82 CNN, kNN, SVM, RF, GPR https://arxiv.org/abs/1712.02777
Return of the features. Efficient feature selection and interpretation for photometric redshifts kNN https://arxiv.org/abs/1803.10032
Photometric redshifts from SDSS images using a Convolutional Neural Network CNN https://arxiv.org/abs/1806.06607
Estimating redshift distributions using Hierarchical Logistic Gaussian processes GPR https://arxiv.org/abs/1904.09988
Gaussian Mixture Models for Blended Photometric Redshifts GMM https://arxiv.org/abs/1907.10572
Photometric Redshift Calibration with Self Organising Maps SOM https://arxiv.org/abs/1909.09632
PS1-STRM: Neural network source classification and photometric redshift catalogue for PS1 NN https://arxiv.org/abs/1910.10167
Reliable Photometric Membership (RPM) of Galaxies in Clusters. I. A Machine Learning Method and its Performance in the Local Universe SVM https://arxiv.org/abs/2002.07263
PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys CNN https://arxiv.org/abs/2003.10766
The PAU Survey: Photometric redshifts using transfer learning from simulations MDN https://arxiv.org/abs/2004.07979
KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps SOM https://arxiv.org/abs/2005.04207
Determining the systemic redshift of Lyman-α emitters with neural networks and improving the measured large-scale clustering NN https://arxiv.org/abs/2005.12931
Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4 RF, XGBoost, NN https://arxiv.org/abs/2010.13857
Photometric Redshift Estimation with a Convolutional Neural Network: NetZ CNN https://arxiv.org/abs/2011.12312
A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest RF https://arxiv.org/abs/2012.05928
Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS) NN https://arxiv.org/abs/2101.02532
Estimating Galactic Distances From Images Using Self-supervised Representation Learning SSL https://arxiv.org/abs/2101.04293
QSO photometric redshifts using machine learning and neural networks kNN, DT, NN https://arxiv.org/abs/2102.09177
Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation RF, BDT, kNN https://arxiv.org/abs/2104.01875
Z-Sequence: Photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours kNN https://arxiv.org/abs/2104.11335
Probabilistic photo-z machine learning models for X-ray sky surveys RF https://arxiv.org/abs/2107.01891
Non-Sequential Neural Network for Simultaneous, Consistent Classification and Photometric Redshifts of OTELO Galaxies NN https://arxiv.org/abs/2108.09415
Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts NN https://arxiv.org/abs/2108.13260
Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images RF, CNN https://arxiv.org/abs/2109.02503
Estimation of Photometric Redshifts. I. Machine Learning Inference for Pan-STARRS1 Galaxies Using Neural Networks NN https://arxiv.org/abs/2110.05726
Photometric Redshift Estimation of BASS DR3 Quasars by Machine Learning RF, XGBoost https://arxiv.org/abs/2110.14951
Photometric redshifts for the S-PLUS Survey: is machine learning up to the task? GP, NN, MDN https://arxiv.org/abs/2110.13901
Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z MDN https://arxiv.org/abs/2111.12118
Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network CNN https://arxiv.org/abs/2112.03939
Estimation of Photometric Redshifts. II. Identification of Out-of-Distribution Data with Neural Networks NN https://arxiv.org/abs/2112.07104
Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates NN https://arxiv.org/abs/2112.07811
Extracting Photometric Redshift from Galaxy Flux and Image Data using Neural Networks in the CSST Survey NN https://arxiv.org/abs/2112.08690
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty Estimates Using Bayesian Neural Networks BNN https://arxiv.org/abs/2202.07121
The sensitivity of GPz estimates of photo-z posterior PDFs to realistically complex training set imperfections GP https://arxiv.org/abs/2202.12775
Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods CNN https://arxiv.org/abs/2202.09964
Estimating Galaxy Redshift in Radio-Selected Datasets using Machine Learning kNN, RF https://arxiv.org/abs/2202.13504
All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8 GMM https://arxiv.org/abs/2203.01949
Photometric redshift-aided classification using ensemble learning GBM https://arxiv.org/abs/2204.02080
Deep Learning nearby galaxy peculiar velocities CNN https://arxiv.org/abs/2204.09135
Photometric redshifts for quasars from WISE-PS1-STRM XGBoost https://arxiv.org/abs/2206.01440
Photometric redshift estimates using Bayesian neural networks in the CSST survey BNN https://arxiv.org/abs/2206.13696

Other Observational

Title ML technique(s) used arxiv link
Use of neural networks for the identification of new z>=3.6 QSOs from FIRST-SDSS DR5 NN https://arxiv.org/abs/0809.0547
Estimating the Mass of the Local Group using Machine Learning Applied to Numerical Simulations NN https://arxiv.org/abs/1606.02694
A probabilistic approach to emission-line galaxy classification GMM https://arxiv.org/abs/1703.07607
Deep Learning of Quasar Spectra to Discover and Characterize Damped Lya Systems CNN https://arxiv.org/abs/1709.04962
An automatic taxonomy of galaxy morphology using unsupervised machine learning SOM https://arxiv.org/abs/1709.05834
Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification RF, ADA, EXT, BDT, MINT, TINT https://arxiv.org/abs/1712.03970
Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning OLR, RF, BDT, kNN, SVM, NN https://arxiv.org/abs/1803.08334
Star-galaxy classification in the Dark Energy Survey Y1 dataset SVM, ADA https://arxiv.org/abs/1805.02427
Classifying galaxy spectra at 0.5<z<1 with self-organizing maps SOM https://arxiv.org/abs/1805.07845
Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies CNN https://arxiv.org/abs/1805.12008
Knowledge transfer of Deep Learning for galaxy morphology from one survey to another CNN https://arxiv.org/abs/1807.00807
Classification of Broad Absorption Line Quasars with a Convolutional Neural Network CNN https://arxiv.org/abs/1901.04506
Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning GAN https://arxiv.org/abs/1904.10286
Deconfusing intensity maps with neural networks CNN https://arxiv.org/abs/1905.10376
Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets RCNN https://arxiv.org/abs/1906.08784
Improving Galaxy Clustering Measurements with Deep Learning: analysis of the DECaLS DR7 data NN https://arxiv.org/abs/1907.11355
What can Machine Learning tell us about the background expansion of the Universe? GA https://arxiv.org/abs/1910.01529
A deep learning approach to cosmological dark energy models BNN+RNN https://arxiv.org/abs/1910.02788
Reconstructing Functions and Estimating Parameters with Artificial Neural Network: a test with Hubble parameter and SNe Ia NN, RNN, LSTM, GRU https://arxiv.org/abs/1910.03636
Multi-wavelength properties of radio and machine-learning identified counterparts to submillimeter sources in S2COSMOS SVM, XGBoost https://arxiv.org/abs/1910.03596
Machine learning computation of distance modulus for local galaxies kNN, BDT, NN https://arxiv.org/abs/1910.07317
MILCANN : A neural network assessed tSZ map for galaxy cluster detection NN https://arxiv.org/abs/1702.00075
Machine Learning meets the redshift evolution of the CMB Temperature GA https://arxiv.org/abs/2002.12700
Inverse Cosmography: testing the effectiveness of cosmographic polynomials using machine learning RNN+BNN https://arxiv.org/abs/2005.02807
Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach VAE https://arxiv.org/abs/2005.12039
Fully probabilistic quasar continua predictions near Lyman-α with conditional neural spline flows NF https://arxiv.org/abs/2006.00615
Artificial intelligence and quasar absorption system modelling; application to fundamental constants at high redshift AI https://arxiv.org/abs/2008.02583
Deep learning the astrometric signature of dark matter substructure CNN https://arxiv.org/abs/2008.11577
Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning VAE https://arxiv.org/abs/2009.11932
Deep Learning for Line Intensity Mapping Observations: Information Extraction from Noisy Maps GAN https://arxiv.org/abs/2010.00809
Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks CNN https://arxiv.org/abs/2010.03762
Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events GP, GA https://arxiv.org/abs/2011.02718
Survey2Survey: A deep learning generative model approach for cross-survey image mapping AE, GAN https://arxiv.org/abs/2011.07124
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning CNN https://arxiv.org/abs/2011.12437
Model independent calibrations of gamma ray bursts using machine learning RF, NN https://arxiv.org/abs/2011.13590
Self-Supervised Representation Learning for Astronomical Images SSL https://arxiv.org/abs/2012.13083
An Active Galactic Nucleus Recognition Model based on Deep Neural Network NN https://arxiv.org/abs/2101.06683
A Machine Learning Approach to Measuring the Quenched Fraction of Low-Mass Satellites Beyond the Local Group NN https://arxiv.org/abs/2102.05050
The PAU survey: Estimating galaxy photometry with deep learning CNN https://arxiv.org/abs/2104.02778
Capturing the physics of MaNGA galaxies with self-supervised Machine Learning SSL https://arxiv.org/abs/2104.08292
Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks GAN, AE https://arxiv.org/abs/2105.02434
Euclid preparation: XVI. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models VAE https://arxiv.org/abs/2105.12149
Planck Limits on Cosmic String Tension Using Machine Learning CNN https://arxiv.org/abs/2106.00059
Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques CNN https://arxiv.org/abs/2107.00385
Galaxy Deblending using Residual Dense Neural networks RDN https://arxiv.org/abs/2109.09550
An astronomical image content-based recommendation system using combined deep learning models in a fully unsupervised mode SOM https://arxiv.org/abs/2103.00276
Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders AE https://arxiv.org/abs/2110.02232
Deep Transfer Learning for Blended Source Identification in Galaxy Survey Data CNN https://arxiv.org/abs/2110.08180
Explaining deep learning of galaxy morphology with saliency mapping CNN https://arxiv.org/abs/2110.08288
SDSS-IV DR17: Final Release of MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs CNN https://arxiv.org/abs/2110.10694
Realistic galaxy image simulation via score-based generative models DDPM https://arxiv.org/abs/2111.01713
A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification CNN https://arxiv.org/abs/2111.04353
Weight Pruning and Uncertainty in Radio Galaxy Classification CNN https://arxiv.org/abs/2111.11654
Probabilistic segmentation of overlapping galaxies for large cosmological surveys U-Net https://arxiv.org/abs/2111.15455
Radio Galaxy Zoo: Giant Radio Galaxy Classification using Multi-Domain Deep Learning CNN https://arxiv.org/abs/2112.03564
Deep Learning of DESI Mock Spectra to Find Damped Lyα Systems CNN https://arxiv.org/abs/2201.00827
Astronomical Image Colorization and upscaling with Generative Adversarial Networks GAN https://arxiv.org/abs/2112.13865
Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending CNN https://arxiv.org/abs/2201.04714
A Hitchhiker's Guide to Anomaly Detection with Astronomaly CNN, NN https://arxiv.org/abs/2201.10189
Classifying Galaxy Morphologies with Few-Shot Learning CNN https://arxiv.org/abs/2202.08172
ShapeNet: Shape Constraint for Galaxy Image Deconvolution CNN https://arxiv.org/abs/2203.07412
Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks CNN https://arxiv.org/abs/2203.09908
Automatic detection of low surface brightness galaxies from SDSS images CNN https://arxiv.org/abs/2203.16813
Red Dragon: A Redshift-Evolving Gaussian Mixture Model for Galaxies GMM https://arxiv.org/abs/2204.10141
Classification of Galaxy Cluster Membership with Machine Learning RF https://arxiv.org/abs/2205.01700
Redshifts of radio sources in the Million Quasars Catalogue from machine learning NN https://arxiv.org/abs/2205.04587
Solving the H0 tension in f(T) Gravity through Bayesian Machine Learning NN https://arxiv.org/abs/2205.06252
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks GAN https://arxiv.org/abs/2205.06758
Identifying outliers in astronomical images with unsupervised machine learning kNN, AE https://arxiv.org/abs/2205.09760
Robust Neural Network-Enhanced Estimation of Local Primordial Non-Gaussianity NN https://arxiv.org/abs/2205.12964
The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques AE, kNN https://arxiv.org/abs/2206.06165
The Dynamical Mass of the Coma Cluster from Deep Learning CNN https://arxiv.org/abs/2206.14834
Radio Galaxy Classification with wGAN-Supported Augmentation NN, GAN https://arxiv.org/abs/2206.15131
Likelihood-free Inference with Mixture Density Network MDN https://arxiv.org/abs/2207.00185
An Exploration of How Training Set Composition Bias in Machine Learning Affects Identifying Rare Objects NN https://arxiv.org/abs/2207.03207
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks BNN https://arxiv.org/abs/2207.03471
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images VAE https://arxiv.org/abs/2207.05642
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters CNN https://arxiv.org/abs/2207.05107
Bayesian Neural Networks for classification tasks in the Rubin big data era BNN https://arxiv.org/abs/2207.04578

 

Parameter Estimation

Cosmological parameter estimation is the mechanism of inferring the contents and evolution of our universe from observations. This topic is quite broad, and therefore parameter estimation papers with a focus on an individual experiment/dataset can be found in other sections (e.g. the Reionization and 21cm section). Note that there is an abundance of work in this area that is not covered in the few examples below.

Title ML technique(s) used arxiv link
Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest GP https://arxiv.org/abs/1812.04631
Fast likelihood-free cosmology with neural density estimators and active learning MDN, NF (MAF) https://arxiv.org/abs/1903.00007
Accelerated Bayesian inference using deep learning NN https://arxiv.org/abs/1903.10860
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations GP https://arxiv.org/abs/1905.07410
Euclid-era cosmology for everyone: Neural net assisted MCMC sampling for the joint 3x2 likelihood NN https://arxiv.org/abs/1907.05881
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks BNNs https://arxiv.org/abs/1911.08508
Flow-Based Likelihoods for Non-Gaussian Inference NF https://arxiv.org/abs/2007.05535
Nearest Neighbor distributions: new statistical measures for cosmological clustering kNN-CDF https://arxiv.org/abs/2007.13342
Likelihood-free inference with neural compression of DES SV weak lensing map statistics NF https://arxiv.org/abs/2009.08459
Neural networks as optimal estimators to marginalize over baryonic effects NN https://arxiv.org/abs/2011.05992
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks NF https://arxiv.org/abs/2011.05991
Accelerating MCMC algorithms through Bayesian Deep Networks BNN https://arxiv.org/abs/2011.14276
Seeking New Physics in Cosmology with Bayesian Neural Networks I: Dark Energy and Modified Gravity BNN https://arxiv.org/abs/2012.03992
Unsupervised Resource Allocation with Graph Neural Networks GNN https://arxiv.org/abs/2106.09761
Machine-driven searches for cosmological physics IM https://arxiv.org/abs/2107.00657
Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields NN https://arxiv.org/abs/2107.07405
Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis NF https://arxiv.org/abs/2202.05282
LINNA: Likelihood Inference Neural Network Accelerator NN https://arxiv.org/abs/2203.05583
Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation NF https://arxiv.org/abs/2203.07391
DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes CNN https://arxiv.org/abs/2203.09616
Cosmological informed neural networks to solve the background dynamics of the Universe NN https://arxiv.org/abs/2205.02945
Improving cosmological covariance matrices with machine learning AE https://arxiv.org/abs/2205.10881
Bayesian error propagation for neural-net based parameter inference NN https://arxiv.org/abs/2205.11587
CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference NN https://arxiv.org/abs/2205.15726
Neural Posterior Estimation with Differentiable Simulators NF https://arxiv.org/abs/2207.05636
Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo NF https://arxiv.org/abs/2207.05652
pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology NF https://arxiv.org/abs/2207.05660

 


Tools

Contained here are some machine learning tools that are specifically designed for the computational challenges of cosmology.

Title arxiv link
CosmoFlow: Using Deep Learning to Learn the Universe at Scale https://arxiv.org/abs/1808.04728
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications https://arxiv.org/abs/1810.12186
Convolutional Neural Networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis https://arxiv.org/abs/1902.04083
CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers https://arxiv.org/abs/1907.05764
FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver https://arxiv.org/abs/2010.11847
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs https://arxiv.org/abs/2102.02828
Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters https://arxiv.org/abs/2107.00665
Equivariant Networks for Pixelized Spheres https://arxiv.org/abs/2106.06662
 

Public Datasets

Contained here are some cosmological datasets geared towards straightforward use on machine learning applications.

Title arxiv link github link
Aemulus Project https://arxiv.org/abs/1804.05865 https://aemulusproject.github.io/
The Quijote simulations https://arxiv.org/abs/1909.05273 https://github.com/franciscovillaescusa/Quijote-simulations
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations https://arxiv.org/abs/2010.00619 https://www.camel-simulations.org/
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence https://arxiv.org/abs/2109.10915

 


Reviews

Reviews of machine learning in cosmology, and, more broadly, machine learning in astronomy.

Title arxiv link
Data Mining and Machine Learning in Astronomy https://arxiv.org/abs/0906.2173
The Role of Machine Learning in the Next Decade of Cosmology https://arxiv.org/abs/1902.10159
Machine learning and the physical sciences https://arxiv.org/abs/1903.10563
Building Trustworthy Machine Learning Models for Astronomy https://arxiv.org/abs/2111.14566
Machine Learning and Cosmology https://arxiv.org/abs/2203.08056
Astronomical big data processing using machine learning: A comprehensive review https://link.springer.com/article/10.1007/s10686-021-09827-4
Photometric Redshifts for Next-Generation Surveys https://arxiv.org/abs/2206.13633

 


Acknowledgments

Thanks to the following people for bringing additional papers to my attention!

Philippe Berger

Dana Simard

Michelle Ntampaka

Farida Farsian

Celia Escamilla-Rivera

Michaël Defferrard

Farida Farsian

Pranath Reddy

Camille Avestruz

Harry Bevins

T. Lucas Makinen

Francois Lanusse

Snigdha Sen

José Manuel Casas González

Himanshu Tiwari

About

A comprehensive list of published machine learning applications to cosmology