The estimation of precise cosmological parameters has been a major goal that has driven the scientific community to push the limits of our knowledge and capabilities. Significant progress in recent years has brought us closer to understanding the fundamental properties of the universe. Current and forthcoming cosmological surveys such as DESI, Euclid, SPHEREX, and the Roman space telescope will allow us to observe a large fraction of the sky and provide very detailed maps of the largescale structure of the universe. The vast amounts of data collected from these surveys, i.e. all the smallscale modes, cannot be analysed using fully analytical models. This constitutes a missed opportunity to shed light on longstanding cosmological problems which would benefit from higherstatisticalsignificance measurements, such as cosmological tensions, studying the properties of neutrinos and primordial nongaussianity. In this work, we address such questions by applying a set of different estimators to the Quijotepng and Quijotemassive neutrinos Nbody simulations at small, nonlinear cosmological scales, up to kmax = 0.5 hMpc−1 to find the best approach to constrain Primordial nonGaussianity (PNG) signatures from early universe physics and total neutrino mass (Mν) respectively. We use various summary statistics including the power spectrum, modal bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum to train deep neural networks (NN) and perform likelihoodfree inference of cosmological, PNG, and Mν parameters. We also look into a thorough comparison of summary statistics to determine their optimal combination in terms of PNG and Mν parameters sensitivity.
The estimation of precise cosmological parameters has been a major goal that has driven the scientific community to push the limits of our knowledge and capabilities. Significant progress in recent years has brought us closer to understanding the fundamental properties of the universe. Current and forthcoming cosmological surveys such as DESI, Euclid, SPHEREX, and the Roman space telescope will allow us to observe a large fraction of the sky and provide very detailed maps of the largescale structure of the universe. The vast amounts of data collected from these surveys, i.e. all the smallscale modes, cannot be analysed using fully analytical models. This constitutes a missed opportunity to shed light on longstanding cosmological problems which would benefit from higherstatisticalsignificance measurements, such as cosmological tensions, studying the properties of neutrinos and primordial nongaussianity. In this work, we address such questions by applying a set of different estimators to the Quijotepng and Quijotemassive neutrinos Nbody simulations at small, nonlinear cosmological scales, up to kmax = 0.5 hMpc−1 to find the best approach to constrain Primordial nonGaussianity (PNG) signatures from early universe physics and total neutrino mass (Mν) respectively. We use various summary statistics including the power spectrum, modal bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum to train deep neural networks (NN) and perform likelihoodfree inference of cosmological, PNG, and Mν parameters. We also look into a thorough comparison of summary statistics to determine their optimal combination in terms of PNG and Mν parameters sensitivity.
Extracting cosmological information at nonlinear scales using machine learning.
DUBEY, SUPRIO
2023/2024
Abstract
The estimation of precise cosmological parameters has been a major goal that has driven the scientific community to push the limits of our knowledge and capabilities. Significant progress in recent years has brought us closer to understanding the fundamental properties of the universe. Current and forthcoming cosmological surveys such as DESI, Euclid, SPHEREX, and the Roman space telescope will allow us to observe a large fraction of the sky and provide very detailed maps of the largescale structure of the universe. The vast amounts of data collected from these surveys, i.e. all the smallscale modes, cannot be analysed using fully analytical models. This constitutes a missed opportunity to shed light on longstanding cosmological problems which would benefit from higherstatisticalsignificance measurements, such as cosmological tensions, studying the properties of neutrinos and primordial nongaussianity. In this work, we address such questions by applying a set of different estimators to the Quijotepng and Quijotemassive neutrinos Nbody simulations at small, nonlinear cosmological scales, up to kmax = 0.5 hMpc−1 to find the best approach to constrain Primordial nonGaussianity (PNG) signatures from early universe physics and total neutrino mass (Mν) respectively. We use various summary statistics including the power spectrum, modal bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum to train deep neural networks (NN) and perform likelihoodfree inference of cosmological, PNG, and Mν parameters. We also look into a thorough comparison of summary statistics to determine their optimal combination in terms of PNG and Mν parameters sensitivity.File  Dimensione  Formato  

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https://hdl.handle.net/20.500.12608/68240