Cosmological data are undergoing dramatic growth in size and complexity as detectors, telescopes, and computers become ever more powerful. Modern telescopes produce terabytes of data per observation, and over the next decade, the data volume is expected to enter the petabyte domain. In light of a large amount of new cosmological data available in the next decade, the need for standard and non-standard cosmological simulations will be essential for making theoretical predictions, generating mock data, computing covariance matrices and optimizing observational strategies. Unfortunately, producing such simulations requires high computational resources; thus the development of new computational methods are needed to accelerate this process. In this talk I will propose a new deep learning-based approach to find the mapping between the positions of particles in simulations with massless and massive neutrinos. I will also discuss the possibility of developing a Convolutional Neural Network emulator to produce accurate N-body simulations.
Transmisión vía Youtube en: bit.ly/YouTube_ICF
Participante: Dra. Elena Giusarama
Institución: Michigan Technological University (Estados Unidos)
Fecha y hora: Este evento terminó el Miércoles, 08 de Septiembre de 2021