Université de GenèveDépartement de Physique ThéoriqueCAP Genève

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Machine Learning

Recent publications and presentations on this topic
A representation learning approach to probe for dynamical dark energy in matter power spectra
Date: 17. October 2023
Members involved: Davide Piras, Lucas Lombriser
Topics: Large-scale structure, Dark energy, Non-linear Power Spectrum, Machine Learning
Type: Publication
A robust estimator of mutual information for deep learning interpretability
Date: 24. March 2023
Members involved: Davide Piras
Topics: Machine Learning, Information Theory
Type: Publication
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Date: 3. March 2023
Members involved: Davide Piras
Topics: Machine Learning, Cosmic Large Scale Structure, N-body simulations
Type: Publication
Euclid: Forecast constraints on consistency tests of the ΛCDM model
Date: 25. October 2021
Members involved: Lucas Lombriser, Martin Kunz
Topics: inhomogeneous cosmology, Local large-scale voids, Dark energy, Machine Learning
Type: Publication
Seeking New Physics in Cosmology with Bayesian Neural Networks I: Dark Energy and Modified Gravity
Date: 9. December 2020
Members involved: Michele Mancarella, Joe Kennedy, Benjamin Bose, Lucas Lombriser
Topics: Large-scale structure, Neural Networks, Dark energy, Modified gravity, Machine Learning
Type: Publication
Constraining the primordial black hole scenario with Bayesian inference and machine learning: the GWTC-2 gravitational wave catalog
Date: 4. November 2020
Members involved: Valerio De Luca, Gabriele Franciolini, Antonio Riotto
Topics: Primordial Black Holes, Gravitational waves, Machine Learning
Type: Publication

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