DS0706 -

Big dataset, Big simulations, Big bang, Big problems: Algorithms of Bayesian reconstruction constrained by physics, application to cosmological data analysis – BIG4

Submission summary

The BIG4 project aims at developing new algorithms of statistical reconstruction of fields on structured grids, such as images and density fields and to also provide an analysis environment based on Web technologies . The project will rely both on the physics of phenomena to reduce the uncertainties and scaling properties of the likelihood to increase the computational speed. Employing techniques from the family of Hamiltonian sampling methods we are able to solve problems including non-linear physical dynamics with millions of parameters. This project intends to revolutionize data analysis in astronomy, for which the data flow is always increasing, notably with the Euclid mission and the LSST project. Nevertheless, BIG4 will be rooted in existing and forthcoming data from surveys like SDSS3, SDSS4 and CosmicFlows-3.

The project will also impact other scientific fields, such as medical imagery, seismology and climatology. All these scientific fields are facing problems of reconstruction and visualization of data that are related to the underlying physical fields in a non-linear way

Finally, we will develop new visualization tools of reconstructed density fields and provide them to the community. These tools will rely on latest WebGL technology and the modules developped for it by the community. These tools will facilitate the visualization of probability distributions which depend on millions of parameters without the necessity to download the raw reconstruction data.

Project coordinator

Monsieur Guilhem Lavaux (INSTITUT D'ASTROPHYSIQUE DE PARIS)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

IAP INSTITUT D'ASTROPHYSIQUE DE PARIS

Help of the ANR 316,278 euros
Beginning and duration of the scientific project: December 2016 - 48 Months

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