Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics

Session

Cosmology

Jun 23, 2022, 11:10 AM

Buffon Amphitheater (APC laboratory, Université Paris Cité)

129. Unraveling the role of cosmic velocity field in dark matter halo mass function using deep learning (cosmo1)

Erfan Abbasgholinezhad (Sharif Uniersity of Technology), Saba Etezad Razavi (Sharif University of Technology)

6/23/22, 11:10 AM

Talk

112. Graph-Convolutional Neural Networks for large-scale structure clustering (cosmo2)

Farida Farsian (Bologna University)

6/23/22, 11:30 AM

Talk

115. Maximum-A-posteriori estimate with Deep generative NEtworks for Source Separation (MADNESS) (cosmo5)

Biswajit Biswas (APC)

6/23/22, 11:50 AM

Talk

116. Interpreting non-Gaussian posterior distributions of cosmological parameters with normalizing flows (cosmo42)

Cyrille Doux (Laboratoire de Physique Subatomique et de Cosmologie)

6/23/22, 12:10 PM

Talk

111. Deblending Galaxies with Generative Adversarial Networks (cosmo_remote1)

Shoubaneh Hemmati (shemmati@caltech.edu)

6/23/22, 4:20 PM

Talk

133. Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment (cosmo_remote2)

Yesukhei Jagvaral

6/23/22, 4:40 PM

Talk

70. COSMOPOWER: Deep Learning - accelerated Bayesian inference from next-generation cosmological surveys

Alessio Spurio Mancini (University College London)

6/23/22, 5:00 PM

Cosmology

Talk

Next-generation Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys will provide us with unprecedented levels of precision in the final constraints on our cosmological model. However, the computational challenges posed by these enormous datasets dangerously hinder their analysis within a Bayesian framework for rigorous uncertainty propagation – a condition necessary to...

54. Deblending Galaxies with Generative Adversarial Networks (cosmo_remote1)

Shoubaneh Hemmati (shemmati@caltech.edu)

Cosmology

Talk

Deep generative models including generative adversarial networks (GANs) are powerful unsupervised tools in learning the distributions of data sets. Various applications of GANs such as image generation, data/image to image translation, feature transfer, or super resolution could notably enhance astronomical data sets. Building a simple GAN architecture in PyTorch and training on the CANDELS...

56. Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment (cosmo_remote2)

Yesukhei Jagvaral

Cosmology

Talk

In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the Universe with realistic galaxy populations are required. In particular, the tendency of galaxies to naturally align towards overdensities, an effect called intrinsic alignments (IA), can be a major source of systematics in the weak lensing analysis. As the details of galaxy formation and evolution...

77. Unraveling the role of cosmic velocity field in dark matter halo mass function using deep learning (cosmo1)

Saba Etezad Razavi (Sharif University of Technology), Mr Erfan Abbasgholinezhad (Sharif Uniersity of Technology)

Cosmology

Talk

Understanding dark matter halo formation is essential for understanding the formation of galaxies to test our cosmological model.
In the last decades, there has been a continued effort to investigate large-scale structure formation using the LSS surveys and large cosmological simulations.
Despite the success of newly developed simulations to predict halo formation, they usually lack the...