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
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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Varma Sreedevi,Fairbairn Malcolm, Figueroa Julio
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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We have shown that neural networks are able to accurately emulate the output of expensive N-body simulations
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Noah Kasmanoff,Francisco Villaescusa-Navarro, Jeremy Tinker,Shirley Ho
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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Digvijay Wadekar,Francisco Villaescusa-Navarro,Shirley Ho, Laurence Perreault-Levasseur
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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Ji Won Park, Sebastian Wagner-Carena, Simon Birrer,Philip J. Marshall, Joshua Yao-Yu Lin,Aaron Roodman
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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Madhurima Choudhury, Atrideb Chatterjee,Abhirup Datta,Tirthankar Roy Choudhury
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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Orlando Luongo, Marco Muccino
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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Luis Fernando Machado Poletti Valle, Camille Avestruz,David J. Barnes, Arya Farahi,Erwin T. Lau,Daisuke Nagai
We present interpretable machine learning models that predict gas shapes in dark matter haloes
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S. Schuldt,S. H. Suyu, R. Cañameras,S. Taubenberger, T. Meinhard,L. Leal-Taixé, B. C. Hsieh
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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Carolina Parroni, Edouard Tollet, Vincenzo F. Cardone,Roberto Maoli,Roberto Scaramella
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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Francisco Villaescusa-Navarro,Benjamin D. Wandelt, Daniel Anglés-Alcázar,Shy Genel, Jose Manuel Zorrilla Mantilla,Shirley Ho,David N. Spergel
In this work we show that neural networks can achieve that
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Niall Jeffrey, Benjamin D. Wandelt
Density estimation likelihood-free inference in cosmology has generally been limited to a few parameters
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Rubén Arjona, Hai-Nan Lin,Savvas Nesseris, Li Tang
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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Nicoletta Krachmalnicoff, Giuseppe Puglisi
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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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
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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Joshua Yao-Yi Lin, Hang Yu, Warren Morningstar, Jian Peng,Gilbert Holder
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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Chirag Modi, Francois Lanusse,Uros Seljak
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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Adam Coogan, Konstantin Karchev,Christoph Weniger
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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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
Authors
Shirley Ho
Paper 9
Barnabás Póczos
Paper 5
Risa Wechsler
Paper 4
Hendrik Hildebrandt
Paper 4
Adam D. Myers
Paper 4
Benjamin D. Wandelt
Paper 4
Ofer Lahav
Paper 4
Jochen Weller
Paper 3
M. Carrasco Kind
Paper 3