Gaussian Process Regression: An Application to Radio Cosmology
Paula Soares
sciencesconf.org:ml-iap2021:365641 |
Machine learning within the THREEHUNDERD simulation project
Daniel de Andres, Gustavo Yepes, Weiguang Cui, Marco De Petris, Florian Ruppin
sciencesconf.org:ml-iap2021:365878 |
Considerations for optimizing photometric classification of supernovae from the Rubin Observatory
Catarina S. Alves, Hiranya V. Peiris, Michelle Lochner, Jason D. McEwen, Tarek Allam Jr, Rahul Biswas
sciencesconf.org:ml-iap2021:366394 |
Inferring the assembly and merger histories of galaxies with the IllustrisTNG simulations and machine learning
Lukas Eisert, Annalisa Pillepich, Dylan Nelson, Ralf Klessen
sciencesconf.org:ml-iap2021:367649 |
Search for galaxy-scale strong lenses in DES and CFIS
Frédéric Courbin
sciencesconf.org:ml-iap2021:367683 |
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Davide Piras, Benjamin Joachimi, Francisco Villaescusa-Navarro
sciencesconf.org:ml-iap2021:367866 |
Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations
Denise lanzieri, François Lanusse, Chirag Modi
sciencesconf.org:ml-iap2021:367889 |
Machine Learning Calibration of Cosmic Shear Redshift Distributions
Angus Wright
sciencesconf.org:ml-iap2021:367923 |
Investigations for LSST with Machine Learning: Strong Lens Mass Modeling and Photometric Redshift estimation
Stefan Schuldt, Sherry Suyu, Raoul Canameras, Yiping Shu, Stefan Taubenberger
sciencesconf.org:ml-iap2021:367956 |
Single frequency CMB polarized foreground marginalization with a single training image
Niall Jeffrey
sciencesconf.org:ml-iap2021:367971 |
STag: Supernova Tagging and Classification
William Davison, David Parkinson, Brad E. Tucker
sciencesconf.org:ml-iap2021:367977 |
hyphy: Mapping Dark Matter to Hydrodynamics with Posterior Inference
Benjamin A Horowitz
sciencesconf.org:ml-iap2021:368033 |
Probabilistic Mapping of Dark Matter by Neural Score Matching
Benjamin Remy, François Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato
sciencesconf.org:ml-iap2021:368048 |
Dark Substructure Sensitivity in the Euclid Survey with Machine Learning
Conor O'Riordan
sciencesconf.org:ml-iap2021:368064 |
Use data not models -- Lensing of '69 as an example for data-driven inference
Jenny Wagner
sciencesconf.org:ml-iap2021:368087 |
Accelerating the modeling of HI on cosmological scales via Deep Learning
Robert Feldmann, Mauro Bernardini, Joachim Stadel, Lucio Mayer, James Bullock, Daniel Anglés-Alcázar, Michael Boylan-Kolchin
sciencesconf.org:ml-iap2021:368090 |
The Dark Quest project for cosmological emulation
Takahiro Nishimichi, Masahiro Takada, Ryuichi Takahashi, Ken Osato, Masato Shirasaki, Taira Oogi, Hironao Miyatake, Masamune Oguri, Ryoma Murata, Yosuke Kobayashi, Naoki Yoshida, Sunao Sugiyama, Satoshi Tanaka
sciencesconf.org:ml-iap2021:368095 |
Parameter Estimation with Physics Informed Neural Networks
Alex Lague, J. Richard Bond, Renee Hlozek
sciencesconf.org:ml-iap2021:368111 |
‘Deep' vs. 'Shallow' Learning in Cosmological Surveys
Ofer Lahav
sciencesconf.org:ml-iap2021:368114 |
The scattering transform in cosmology, or, a CNN without Training
Sihao Cheng, Brice Ménard, Yuan-Sen Ting, Joan Bruna
sciencesconf.org:ml-iap2021:368116 |
Using convolutional neural networks to identify strong lenses in Euclid and J-PAS
Alberto Manjon García, Jose María Diego Rodriguez, Diego Herranz Muñoz, Helena Domínguez Sánchez, Jesús Vega Ferrero
sciencesconf.org:ml-iap2021:368120 |
Artificial intelligence for accurate weak lensing analyses
Daniel Gruen
sciencesconf.org:ml-iap2021:368185 |
Cosmological constraints from galaxy-galaxy lensing and galaxy clustering with HSC-Y1 and BOSS data: the first application of emulator-based halo model to cosmology analysis
Hironao Miyatake, Sunao Sugiyama, Masahiro Takada, Takahiro Nishimichi, Yosuke Kobayashi, Surhud More, Naoki Yoshida
sciencesconf.org:ml-iap2021:368192 |
A wavelet based generative model for high resolution cosmological maps
Jed Homer, Daniel Gruen, Oliver Friedrich
sciencesconf.org:ml-iap2021:368214 |
Learning from 3D tomographic 21cm maps
Caroline Heneka
sciencesconf.org:ml-iap2021:368244 |
Photometric redshifts for cosmology: improving accuracy and uncertainty estimates using Bayesian Neural Networks
Evan Jones, Tuan Do, Bernie Boscoe, Jack Singal, Yujie Wan, Zooey Nguyen
sciencesconf.org:ml-iap2021:368269 |
Superresolving Herschel imaging: a proof of concept using Deep Neural Networks
Lynge Lauritsen, Hugh Dickinson, Jane Bromley, Stephen Serjeant, Chen-Fatt Lim, Zhen-Kai Gao, Wei-Hao Wang
sciencesconf.org:ml-iap2021:368272 |
Machine learning for experimental design: stress-testing redshift uncertainty quantification and propagation with Redshift Assessment Infrastructure Layers (RAIL)
Alex Malz
sciencesconf.org:ml-iap2021:368273 |
Photometric redshift calibration with simulated annealing and self-organising maps
Benjamin Stölzner
sciencesconf.org:ml-iap2021:368290 |
Predicting Physical Properties of Lyman Alpha Forest with Deep Learning
Ting-Yun Cheng, Ryan Cooke
sciencesconf.org:ml-iap2021:368298 |
Detection of the Damped Lyman-alpha systems in quasar spectra with machine learning algorithms
ting tan, Yuankang Liu, Christophe Balland
sciencesconf.org:ml-iap2021:368304 |
A new catalog of 343,000 quasars with their photometric redshifts derived with machine learning from the Kilo Degree Survey
Szymon Nakoneczny, Maciej Bilicki, Agnieszka Pollo, Marika Asgari, Andrej Dvornik, Thomas Erben, Benjamin Giblin, Catherine Heymans, Hendrik Hildebrandt, Arun Kannawadi, Koen Kuijken, Nicola Napolitano, Edwin Valentijn
sciencesconf.org:ml-iap2021:368306 |
Galaxy Cluster Mass Estimation Using Deep Learning
Matthew Ho, Michelle Ntampaka, Markus Michael Rau, Arya Farahi, Hy Trac, Barnabas Poczos
sciencesconf.org:ml-iap2021:368314 |
Bayesian Inference Acceleration: a Deep Learning Approach
Marco Bonici
sciencesconf.org:ml-iap2021:368320 |
Machine learning-infused cluster cosmology
Stéphane Ilic, Simona Mei
sciencesconf.org:ml-iap2021:368326 |
Applying Deep Neural Network to dark matter halo catalogues to constrain the dark energy equation-of-state parameters
Farida Farsian, Lauro Moscardini, Federico Marulli, Carlo Giocoli
sciencesconf.org:ml-iap2021:368405 |
Emulating the void density profile and the void-size function
Giorgia Pollina
sciencesconf.org:ml-iap2021:368424 |
CosmoPower: deep learning emulation of cosmological power spectra for accelerated Bayesian inference from next-generation surveys
Alessio Spurio Mancini
sciencesconf.org:ml-iap2021:368826 |
LyAl-Net: A high-efficiency Lyman-α forest simulation with neural network
Chotipan Boonkongkird
sciencesconf.org:ml-iap2021:369276 |
Review on cosmology and machine learning
Benjamin Wandelt
sciencesconf.org:ml-iap2021:380968 |
Review on machine learning and cosmological simulations
Francisco Villaescusa-Navarro
sciencesconf.org:ml-iap2021:383245 |
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