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iDark

The intelligent Dark Matter survey

Astronomical observations have established that more than 80% of all matter in the Universe is made up of Dark Matter (DM). The determination of the nature of Dark Matter is one of the most important questions in Physics and Astronomy; it will most likely be the result of a combination of all worldwide available experimental data. Combining the worldwide data within the most general models of Dark Matter was the objective of this project. This will test the models, determine the allowed parameter space for Dark Matter and help focus the effort for experimental searches. Finding viable solutions and exploring in a statistically convergent manner huge DM-model parameter spaces is the challenge which we like to attack with advanced eScience methods. Technical solutions to these questions have also multiple applications in society.

We developed new algorithms to find DM solutions in large multidimensional parameter spaces. Furthermore we developed SUSY-AI to accelerate the computing machinery for DM searches. Finally we could develop a prototype for the first web-accessible) “DM model” database.

With the help of such eScience machinery we will establish one the most promising ways to pinpoint DM in the upcoming years.

Participating organisations

Radboud University Nijmegen
Netherlands eScience Center
NIKHEF
Natural Sciences & Engineering
Natural Sciences & Engineering

Impact

Output

Team

SC
Sascha Caron
Principal investigator
Radboud Universiteit Nijmegen
Faruk Diblen
Faruk Diblen
eScience Research Engineer
Netherlands eScience Center
Jisk Attema
Jisk Attema
Senior eScience Research Engineer
Netherlands eScience Center
LH
Luc Hendriks
PhD student
Radboud University Nijmegen
BS
Bob Stienen
PhD student
Radboud University Nijmegen
Rena Bakhshi
Rena Bakhshi
eScience Coordinator
Netherlands eScience Center
TH
Tom Heskes
Co-Applicant
Radboud University Nijmegen

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