MALTA4COSMO · MAchine Learning Techniques and Advanced statistics FOR COSMOlogy

Линиите свързват координатора с партньорите.

Накратко на български

Цел на проекта

Some of the most pressing open problems in modern cosmology concern the nature of the cosmic dark sector, the mismatch between the theoretically predicted and observed values of the cosmological constant, and the growing number of cosmological tensions between independent observational probes. Most notably, the Hubble constant shows a significant and persistent statistical discrepancy between local determinations and those inferred from early-universe Cosmic Microwave Background data. These inconsistencies strongly suggest the need for new physics beyond the standard cosmological model. At the same time, current and forthcoming surveys are generating unprecedented volumes of data, whose accumulation and analysis demand substantial advances in statistical methodology before they can be effectively applied to targeted scientific questions. The MALTA4COSMO project aims to confront these challenges by developing a deep learning framework that incorporates advanced statistical and machine learning techniques, such as neural networks, into cosmological community codes for emulated parameter inference. This approach will enable the identification, in a model-independent way, of generic features of cosmological theories that remain compatible with all available observations. Harnessing the power of deep learning thus provides an innovative arena to explore new physics in the observational sector and to discriminate between competing models that appear degenerate under current methods, advancing data-driven science as a crucial complement to theory-driven approaches. The fellowship will support the researcher’s career development by fostering expertise in machine learning and Bayesian statistics, while also cultivating transversal skills in dissemination and outreach. Moreover, it will expand the researcher’s professional network within Europe and internationally, consolidating a long-term trajectory at the interface of cosmology, data science, and fundamental physics.

Оригинален текст от CORDIS (на английски).

Участници

• UNIVERSITA TA MALTA · MSIDAКоординаторМалта

Връзки

• Виж в CORDIS
• DOI: 10.3030/101272532