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We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing.
Cosmic voids: a novel probe to shed light on our Universe
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
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Bias-variance tradeoff in hybrid generative-discriminative models
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Hybrid discriminative-generative training via contrastive learning
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Training restricted boltzmann machines using approximations to the likelihood gradient
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Rejuvenating the Matter Power Spectrum: Restoring Information with a Logarithmic Density Mapping
Mark C. Neyrinck, István Szapudi, and Alexander S. Szalay · 2009
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Probing cosmology with weak lensing peak counts
Jan M. Kratochvil, Zoltán Haiman, and Morgan May · 2010
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Cfhtlens: cosmological constraints from a combination of cosmic shear two-point and three-point correlations
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ELUCID—Exploring the Local Universe with the Reconstructed Initial Density Field. I. Hamiltonian Markov Chain Monte Carlo Method with Particle Mesh Dynamics
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A marked correlation function for constraining modified gravity models
Martin White · 2016
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nifty galaxy cluster simulations–iii. the similarity and diversity of galaxies and subhaloes
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
Lossless, scalable implicit likelihood inference for cosmological fields
T. Lucas Makinen, Tom Charnock, Justin Alsing, and Benjamin D. Wandelt · 2021
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Likelihood-free inference with neural compression of DES SV weak lensing map statistics
Niall Jeffrey, Justin Alsing, and François Lanusse · 2021
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HIFlow: Generating Diverse HI Maps Conditioned on Cosmology using Normalizing Flow
Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, and Sambatra Andrianomena · 2021
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Multifield cosmology with artificial intelligence
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N Spergel, Yin Li, Benjamin Wandelt, Andrina Nicola, Leander Thiele, Sultan Hassan, Jose Manuel Zorrilla Matilla, et al · 2021
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Mocking the weak lensing universe: The LensTools Python computing package
A. Petri · 2016
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Towards optimal extraction of cosmological information from nonlinear data
Uroš Seljak, Grigor Aslanyan, Yu Feng, and Chirag Modi · 2017
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Iain Murray, and Theo Pavlakou · 2017
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Generative and discriminative text classification with recurrent neural networks
Dani Yogatama, Chris Dyer, Wang Ling, and Phil Blunsom · 2017
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Cosmological constraints from noisy convergence maps through deep learning
Janis Fluri, Tomasz Kacprzak, Alexandre Refregier, Adam Amara, Aurelien Lucchi, and Thomas Hofmann · 2018
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Automatic physical inference with information maximizing neural networks
Tom Charnock, Guilhem Lavaux, and Benjamin D. Wandelt · 2018
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Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions
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How to train your energy-based models
Yang Song and Diederik P Kingma · 2021
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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Representational aspects of depth and conditioning in normalizing flows
Frederic Koehler, Viraj Mehta, and Andrej Risteski · 2021
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The impact of baryons on cosmological inference from weak lensing statistics
Tianhuan Lu and Zoltán Haiman · 2021
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Cosmic shear cosmology beyond two-point statistics: a combined peak count and correlation function analysis of des-y1
Joachim Harnois-Déraps, Nicolas Martinet, Tiago Castro, Klaus Dolag, Benjamin Giblin, Catherine Heymans, Hendrik Hildebrandt, and Qianli Xia · 2021
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Weak lensing scattering transform: dark energy and neutrino mass sensitivity
Sihao Cheng and Brice Ménard · 2021
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Lifting weak lensing degeneracies with a field-based likelihood
Natalia Porqueres, Alan Heavens, Daniel Mortlock, and Guilhem Lavaux · 2022
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Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis
Biwei Dai and Uroš Seljak · 2022
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Higlow: Conditional normalizing flows for high-fidelity hi map modeling
Roy Friedman and Sultan Hassan · 2022
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Core Francisco Park, Erwan Allys, Francisco Villaescusa-Navarro, and Douglas P Finkbeiner · 2022
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Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing
Tianhuan Lu, Zoltán Haiman, and José Manuel Zorrilla Matilla · 2022
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Learning cosmology and clustering with cosmic graphs
Pablo Villanueva-Domingo and Francisco Villaescusa-Navarro · 2022
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Full w cdm analysis of kids-1000 weak lensing maps using deep learning
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi, Aurel Schneider, Alexandre Refregier, and Thomas Hofmann · 2022
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Plausible adversarial attacks on direct parameter inference models in astrophysics
Benjamin Horowitz and Peter Melchior · 2022
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Realistic galaxy image simulation via score-based generative models
Michael J Smith, James E Geach, Ryan A Jackson, Nikhil Arora, Connor Stone, and Stéphane Courteau · 2022
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Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Nayantara Mudur and Douglas P Finkbeiner · 2022
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Dark energy survey year 3 results: Cosmology with peaks using an emulator approach
Dominik Zürcher, Janis Fluri, Raphaël Sgier, Tomasz Kacprzak, Marco Gatti, Cyrille Doux, Lorne Whiteway, Alexandre Refregier, Chihway Chang, Niall Jeffrey, et al · 2022
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Towards an optimal estimation of cosmological parameters with the wavelet scattering transform
Georgios Valogiannis and Cora Dvorkin · 2022
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Cosmological constraints from hsc survey first-year data using deep learning
Tianhuan Lu, Zoltán Haiman, and Xiangchong Li · 2023
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Revisiting discriminative vs. generative classifiers: Theory and implications
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Can diffusion model conditionally generate astrophysical images?
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Diffusion generative modeling for galaxy surveys: emulating clustering for inference at the field level
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Cosmological studies from hsc-ssp tomographic weak-lensing peak abundances
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