Project description
New software to extract info from Cosmic Dawn Survey database
This is where astronomy meets big data mining. While new telescopes are powerful enough to capture the rarest galaxy types, it is a challenge to analyse this data. The EU-funded BESTTIME project will design machine learning software to extract information from the Cosmic Dawn Survey database. Specifically, it will conduct an advanced statistical analysis in unprecedented demographics, such as stellar mass function, of high redshift (z>3), massive (>5e10 Msol) galaxies. The findings will improve our understanding of the physical mechanisms driving star formation in such extreme galaxies, which are thought to contain most of the stellar mass in the early universe.
Objective
An epoch of “big data” mining just started for high redshift astronomy. State-of-the-art photometric surveys observed millions of objects on sky, and next-generation telescopes will provide statistically significant samples even for the rarest galaxy types. However, standard techniques (e.g. methods to fit galaxies' spectral energy distribution) are not optimal to analyse this data, preventing a true scientific breakthrough. Indeed some of the most urgent questions in this research field, concerning the evolution of rare ultra-massive galaxies, may remain unsolved. As the Cosmic Dawn Center (University of Copenhagen) is collecting an unprecedented large sample of photometric and spectroscopic data, this is the best time to devise new tools for a full exploitation of such unique observations.
The proposed project will devise an original machine learning software to efficiently extract information from the Cosmic Dawn Survey database, and an advanced statistical analysis will result in unprecedented demographics (e.g. stellar mass function) of high redshift (z>3), massive (>5e10 Msol) galaxies. By interpreting these measurements through theoretical models, the project will shed light on the physical mechanisms driving star formation in such extreme galaxies, which are thought to contain most of the stellar mass in the early universe. Besides the unparalleled data set, the expertise of S. Toft (host supervisor) and the other members of the Cosmic Dawn Center will be crucial to achieve these goals.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencescomputer and information sciencesdatabases
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesdata sciencedata mining
- natural sciencesphysical sciencesastronomyastrophysicsblack holes
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
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Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
1165 Kobenhavn
Denmark