Project description
Solving the mysteries of the universe with deep learning
Modern cosmology faces significant challenges, particularly regarding the mysterious dark sector and discrepancies such as the conflicting measurements of the Hubble constant. These inconsistencies expose gaps in our understanding of the universe and suggest the need for new physics beyond the current model. For instance, local measurements of the Hubble constant differ significantly from those derived from the early Cosmic Microwave Background data. Such tensions complicate our grasp of cosmic expansion and structure. With this in mind, the EU-funded COSMOMALTA project aims to develop a learning framework that incorporates advanced statistical methods. This approach will enhance the analysis of large observational datasets, allowing for model-independent insights into various cosmological theories. Ultimately, COSMOMALTA will advance our understanding of the universe.
Objective
Some of the biggest open problems in modern cosmology are the nature of the cosmic dark sector, the discrepancy between the theoretically predicted versus the observed value of the cosmological constant, and the growing cosmological discordances and tensions between different observational probes. Notably, the Hubble constant, which describes how fast the Universe is expanding when measured locally, has an enormous statistical disagreement with that inferred from the early Cosmic Microwave Background data. These inconsistencies, in turn, necessitate the formulation of new physics beyond the standard cosmological model. Current and ongoing observations, together with upcoming surveys, will produce large volumes of data, whose accumulation and processing will require an upgradation and increase in the sophistication of our statistical tools before applying them to specific problems. Thus, we propose to build a deep learning architecture using advanced statistics in machine learning algorithms like neural networks to be integrated into cosmological community codes for emulated parameter inference. This will help us to select, in a model-independent way, generic features of some cosmological theories that satisfy all observations. Utilising the power of deep learning will be an ideal space to investigate new physics in the observational sector and discriminate between models that are degenerate in terms of current observational approaches, fostering the development of data-driven science as a valuable companion to the model-driven paradigm. The fellowship will contribute to the researcher's career development by acquiring advanced skills in machine learning approaches using Bayesian statistics and developing skills within the cosmological community through a series of events designed to disseminate his results to the broader public. The project will also serve to consolidate and extend the researcher's network of professional contacts within Europe and beyond.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.4.1 - Widening participation and spreading excellence
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-WIDERA-2023-TALENTS-02
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
MSD 2080 MSIDA
Malta
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.