Machine learning within the THREEHUNDERD simulation project Daniel de Andres, Gustavo Yepes, Weiguang Cui, Marco De Petris, Florian Ruppin sciencesconf.org:ml-iap2021:365878
|
Stratified Learning: A general-purpose method for learning under covariate shift with applications to observational cosmology Roberto Trotta, Maximilian Autenrieth, David van Dyk, David Stenning sciencesconf.org:ml-iap2021:366014
|
Bridging the gap between simulations and survey data - domain adaptation for deep learning in astronomy Aleksandra Ciprijanovic, Diana Kafkes, Kathryn Downey, Sydney Jenkins, Gabriel Nathan Perdue, Sandeep Madireddy, Gregory Snyder, Brian Nord, Travis Johnston sciencesconf.org:ml-iap2021:366128
|
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
|
Real-time science-driven follow-up of survey transients: Optimally augmenting ZTF SN Ia light curves for maximizing SALT2 parameter constraints Niharika Sravan sciencesconf.org:ml-iap2021:367435
|
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
|
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with t-SNE John Weaver, Charles Steinhardt, Jack Maxfield sciencesconf.org:ml-iap2021:367795
|
Bayes vs. machine learning in large-scale survey demographics with eROSITA Johannes Buchner sciencesconf.org:ml-iap2021:367804
|
Multifrequency Point Source detection with Fully-Convolutional Networks: Performance in realistic microwave sky simulations José Manuel Casas, Joaquín González-Nuevo, Laura Bonavera sciencesconf.org:ml-iap2021:367860
|
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
|
Machine Learning Calibration of Cosmic Shear Redshift Distributions Angus Wright sciencesconf.org:ml-iap2021:367923
|
Capturing the physics of MaNGA galaxies with self-supervised Machine Learning Regina Sarmiento, Marc Huertas-Company, Johan H. Knapen sciencesconf.org:ml-iap2021:367943
|
Single frequency CMB polarized foreground marginalization with a single training image Niall Jeffrey sciencesconf.org:ml-iap2021:367971
|
Pushing automated morphological classifications to their limits with the Dark Energy Survey Jesús Vega Ferrero sciencesconf.org:ml-iap2021:367991
|
What drives the scatter in the BPT diagrams? A Machine Learning based analysis Mirko Curti sciencesconf.org:ml-iap2021:367995
|
Reconstructing blended galaxies with Machine Learning Lavanya Nemani, Emiliano Merlin, Adriano Fontana sciencesconf.org:ml-iap2021:368006
|
hyphy: Mapping Dark Matter to Hydrodynamics with Posterior Inference Benjamin A Horowitz sciencesconf.org:ml-iap2021:368033
|
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
|
Identifying strong gravitational lenses in current and future large-scale imaging surveys Raoul Canameras sciencesconf.org:ml-iap2021:368092
|
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
|
A deep transfer learning approach to photospheric parameters of CARMENES target stars Antonio Bello-Garcia, Ordieres-Mere Joaquín, Vera Maria Passegger, Jose Antonio Caballero, Andreas Schweitzer, Ana Gonzalez-Marcos sciencesconf.org:ml-iap2021:368131
|
Source detection through semantic segmentation with convolutional neural networks Maxime Paillassa, Emmanuel Bertin, Herve Bouy sciencesconf.org:ml-iap2021:368135
|
Deep Neural Networks for detection of sources in radio astronomical maps: main challenges and current achievements. Renato Sortino, Daniel Magro, Carmelo Pino, Eva Sciacca, Simone Riggi, Giuseppe Fiameni sciencesconf.org:ml-iap2021:368150
|
Quantifying uncertainty in deep-learning classification of radio galaxies Fiona Porter, Anna Scaife sciencesconf.org:ml-iap2021:368195
|
Inference and probabilistic modelling with machine learning for LISA data analysis Natalia Korsakova sciencesconf.org:ml-iap2021:368198
|
Learning from 3D tomographic 21cm maps Caroline Heneka sciencesconf.org:ml-iap2021:368244
|
Using a series Machine Learning models for the detection of high-redshift Radio Galaxy candidates. Rodrigo Carvajal, José Afonso, Israel Matute, Stergios Amarantidis, Davi Barbosa sciencesconf.org:ml-iap2021:368252
|
Machine learning: lessons learnt with the QUBRICS survey Francesco Guarneri, Giorgio Calderone, Stefano Cristiani, Andrea Grazian, Konstantina Boutsia, Guido Cupani, Fabio Fontanot, Valentina D'Odorico sciencesconf.org:ml-iap2021:368260
|
MINERVA team: Winning the SKA Science Data Challenge 2 with fast dedicated CNN architectures David Cornu, Benoit Semelin, Stephane Aicardi, Xuezhou Lu, Philippe Salomè, Antoine Marchal, Jonathan Freundlich, Françoise Combes sciencesconf.org:ml-iap2021:368267
|
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
|
CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines Chirag Modi, François Lanusse, Uros Seljak, David Spergel, Laurence Perreault-Levasseur sciencesconf.org:ml-iap2021:368289
|
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
|
Machine learning-infused cluster cosmology Stéphane Ilic, Simona Mei sciencesconf.org:ml-iap2021:368326
|
From machine learning to human understanding Daniel Masters sciencesconf.org:ml-iap2021:368330
|
Neural spectrum encoding Peter Melchior sciencesconf.org:ml-iap2021:368337
|
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
|
Simulation and Segmentation with Deep Learning for Euclid Hubert Bretonnière sciencesconf.org:ml-iap2021:369290
|
An hybrid physics-ML framework to model exoplanetary light curves Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou, Ingo Waldmann sciencesconf.org:ml-iap2021:369780
|