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| Author(s): |
Natalí Soler Matubaro de Santi
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Paulo. |
| Institution: | Universidade de São Paulo (USP). Instituto de Física (IF/SBI) |
| Defense date: | 2024-07-04 |
| Examining board members: |
Luis Raul Weber Abramo;
Clécio Roque de Bom;
Raul Esteban Angulo de La Fuente;
Ravi Kiran Sheth;
Laerte Sodre Junior
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| Advisor: | Luis Raul Weber Abramo |
| Abstract | |
The new era of cosmological observations is generating vast amounts of data, increasing the pressure for improvements in both existing and new techniques to analyze this data. Machine Learning (ML) methods are currently at the cutting edge in terms of new techniques and tools, often surpassing traditional methods. In this work, we employ a series of ML techniques to: (1) improve cosmological covariance matrices, (2) investigate the halo-galaxy connection, and (3) perform robust field-level likelihood-free inference with galaxies and halos. Parameter inference is a key aspect in Cosmology, and here we present two different approaches: the use of traditional methods, aimed at obtaining accurate and precise cosmological covariance matrices using image denoising techniques, and a novel approach, which involves deriving parameters directly by converting galaxy/halo catalogs into graphs, without cuts on scale, and then feeding these graphs into graph neural networks to predict the parameters. Simultaneously, the relationship between galaxies and halos is central to describing galaxy formation and is a fundamental step towards extracting precise cosmological information from galaxy maps. We address this problem with a sequence of approaches, ranging from using raw methods and augmenting the data set to stacking methods and converting a regression problem into a classification one, to recover galaxy properties along with their stochasticity. All of these projects aim at improving the extraction of information from simulations by enhancing the accuracy and precision of the derived constraints, thereby impacting cosmological parameters and the halo-galaxy connection. These are the initial steps before applying this new set of innovative methodologies to real data, for both current and next-generation surveys. (AU) | |
| FAPESP's process: | 19/13108-0 - Cosmological covariance matrices and machine learning methods |
| Grantee: | Natali Soler Matubaro de Santi |
| Support Opportunities: | Scholarships in Brazil - Doctorate |