Machine Learning Applications To Cosmology - Must Reading

Machine Learning Applications To Cosmology本论文集收集了学深度学习在宇宙科学中的相关论文

Discovering Symbolic Models from Deep Learning with Inductive Biases

Miles Cranmer,Alvaro Sanchez-Gonzalez, Peter Battaglia,Rui Xu,Kyle Cranmer,David Spergel,Shirley Ho

NIPS 2020, (2020)

We have demonstrated a general approach for imposing physically motivated inductive biases on Graph Networks and Hamiltonian GNs to learn interpretable representations, and potentially improved zero-shot generalization

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Dark Matter Subhalos, Strong Lensing and Machine Learning

Varma Sreedevi,Fairbairn Malcolm, Figueroa Julio

Conclusion

We have generated simulated images of galaxies which have been lensed by dark matter halos with substructure in order to see if machine learning techniques can tell between different kinds of substructure

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Fast and Accurate Non-Linear Predictions of Universes with Deep Learning

Renan Alves de Oliveira,Yin Li,Francisco Villaescusa-Navarro,Shirley Ho,David N. Spergel

Methods

Conclusion

We have shown that neural networks are able to accurately emulate the output of expensive N-body simulations

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dm2gal: Mapping Dark Matter to Galaxies with Neural Networks

Noah Kasmanoff,Francisco Villaescusa-Navarro, Jeremy Tinker,Shirley Ho

Methods

Conclusion

That convolutional neural networks can be used to paint stellar masses into the dark matter field of computationally cheap gravity-only simulations

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Modeling assembly bias with machine learning and symbolic regression

Digvijay Wadekar,Francisco Villaescusa-Navarro,Shirley Ho, Laurence Perreault-Levasseur

Conclusion

We provide physical explanations for the dependences, and model the effect of the halo environment on its Hi mass using machine learning tools like random forests and symbolic regression

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Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

Ji Won Park, Sebastian Wagner-Carena, Simon Birrer,Philip J. Marshall, Joshua Yao-Yu Lin,Aaron Roodman

Methods

Conclusion

We investigate the use of approximate Bayesian neural networks in modeling hundreds of time-delay gravitational lenses for Hubble constant determination

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Using Artificial Neural Networks to extract the 21-cm Global Signal from the EDGES data

Madhurima Choudhury, Atrideb Chatterjee,Abhirup Datta,Tirthankar Roy Choudhury

Conclusion

As the 21-cm signal parameters are closely associated with the physics of the evolution of the signal, the prediction of the signal parameters gives us a direct physical interpretation of the signal

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Model independent calibrations of gamma ray bursts using machine learning

Orlando Luongo, Marco Muccino

Conclusion

gamma-ray bursts would act as perfect standard candles if correlations between GRB photometric and spectroscopic properties would somehow be related to GRB intrinsic properties

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SHAPing the Gas: Understanding Gas Shapes in Dark Matter Haloes with Interpretable Machine Learning

Luis Fernando Machado Poletti Valle, Camille Avestruz,David J. Barnes, Arya Farahi,Erwin T. Lau,Daisuke Nagai

Conclusion

We present interpretable machine learning models that predict gas shapes in dark matter haloes

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Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

S. Schuldt,S. H. Suyu, R. Cañameras,S. Taubenberger, T. Meinhard,L. Leal-Taixé, B. C. Hsieh

Conclusion

With the success of machine learning and especially Convolutional Neural Network in image processing, we investigated a new CNN based technique to estimate the photo-z of a galaxy

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Higher order statistics of shear field: a machine learning approach

Carolina Parroni, Edouard Tollet, Vincenzo F. Cardone,Roberto Maoli,Roberto Scaramella

Conclusion

While the application of machine learning for the prediction of cosmological parameters in the context of weak lensing is not a new concept per se, our work differs from the rest of the literature

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Neural networks as optimal estimators to marginalize over baryonic effects

Francisco Villaescusa-Navarro,Benjamin D. Wandelt, Daniel Anglés-Alcázar,Shy Genel, Jose Manuel Zorrilla Mantilla,Shirley Ho,David N. Spergel

Conclusion

In this work we show that neural networks can achieve that

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Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks

Niall Jeffrey, Benjamin D. Wandelt

Methods

Conclusion

Density estimation likelihood-free inference in cosmology has generally been limited to a few parameters

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

Rubén Arjona, Hai-Nan Lin,Savvas Nesseris, Li Tang

Methods

Conclusion

For both Machine learning approaches we find that the reconstructed errors are consistent with each other, we are confident in our reconstruction as the Genetic Algorithms and the Gaussian Processes are in principle rather different reconstruction methods

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ForSE: a GAN based algorithm for extending CMB foreground models to sub-degree angular scales

Nicoletta Krachmalnicoff, Giuseppe Puglisi

Methods

Conclusion

We present ForSE, a novel Python package which aims at overcoming the current limitations in the simulation of diffuse Galactic radiation, in the context of Cosmic Microwave Background experiments

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Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4

S. J. Nakoneczny,M. Bilicki,A. Pollo,M. Asgari, A. Dvornik,T. Erben, B. Giblin,C. Heymans,H. Hildebrandt, A. Kannawadi,K. Kuijken,N. R. Napolitano

Methods

Conclusion

We found 158k quasar candidates with minimum classification probability p(QSOcand) > 0.9 at r < 22, and a total of 311k quasar candidates with p(QSOcand) > 0.98 for r < 23.5, i.e. in the extension to the close extrapolation data

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Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks

Joshua Yao-Yi Lin, Hang Yu, Warren Morningstar, Jian Peng,Gilbert Holder

Conclusion

We found that neural networks show promising results on detecting multiple dark matter subhalos, and learn to reject the subhalos on the lensing arc of a smooth lens where there is no subhalo

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FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver

Chirag Modi, Francois Lanusse,Uros Seljak

Conclusion

We present FlowPM - a cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations to tackle the unprecedented modeling and analytic challenges posed by the generation of cosmological surveys

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Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies

Adam Coogan, Konstantin Karchev,Christoph Weniger

Conclusion

The mock observation and source are shown in fig. 1, along with the mean observation and source reconstructed with a five-layer GP source model in our first analysis step

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AI-assisted super-resolution cosmological simulations

Yin Li, Yueying Ni,Rupert A. C. Croft,Tiziana Di Matteo,Simeon Bird,Yu Feng

Conclusion

We have shown how a Generative Adversarial Networks super-resolution architecture inspired by StyleGAN2 can be used to enhance cosmological simulations so that they reproduce the appearance and statistics of much higherresolution models

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Keywords

Galaxies: Distances And RedshiftsMethods: Data AnalysisSurveysCosmologyMethods: StatisticalArtificial Neural NetworkGalaxies: StatisticsMachine LearningCataloguesData Analysis

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