Sign in
Ctrl K

DarkGenerators

Interpretable large scale deep generative models for Dark Matter searches

Dark matter (DM) is five times more abundant in the Universe than visible matter. Yet, its nature remains unknown and constitutes one of the most exciting and complex research questions today.

Despite advances in astrophysics, key aspects of the Universe, particularly those relating to Dark Matter, remain largely unknown. This project sought to address these knowledge gaps using advanced computational methods. We developed an algorithm, Truncated Marginal Neural Ratio Estimation, and a software tool, Swyft, for better analyzing high-dimensional models with complex data. The approach short-cuts the high computational costs of traditional techniques (a) by focusing parameter inference and classification tasks on what is scientifically relevant and (b) focusing simulations on the most likely model parameters that describe the data at hand. Our work has accelerated the interpretation of Planck data and improved the extraction of the warm dark matter mass from multiple strong lensing images. These enhancements, along with others from our publications, have significant implications for Dark Matter research, opening new avenues for examining astrophysical observations like strong lensing, 21 cm line data, and gravitational waves. Our findings have broadened the toolkit for probing the unknown aspects of the Universe. This project, thus, marks a substantial step forward in the ongoing exploration of Dark Matter, positioning the scientific community for further breakthroughs in the future.

Participating organisations

Natural Sciences & Engineering
Natural Sciences & Engineering
Netherlands eScience Center
SURF
University of Amsterdam

Impact

Output

Team

CW
Christoph Weniger
Principal investigator
University of Amsterdam
Meiert Grootes
Meiert Grootes
Senior eScience Research Engineer
Netherlands eScience Center
FN
Francesco Nattino
eScience Research Engineer
Netherlands eScience Center
MC
Maxwell Xu CAI
AB
Axel Berg
Programme Director
SURF
Sonja Georgievska
Sonja Georgievska
eScience Research Engineer
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center

Related projects

CORTEX

Self-learning machines hunt for explosions in the universe and speed up innovations in industry and...

Updated 25 months ago
In progress

PADRE - The PetaFLOP AARTFAAC Data-Reduction Engine

Improving the AARTFAAC processing pipeline

Updated 1 month ago
Finished

Scaling up pangenomics for plant breeding

Delivering a pangenome approach that drastically improves the analytical power on plant data

Updated 6 months ago
Finished

EOSCpilot LOFAR

Unlocking the LOFAR Long Term Archive

Updated 21 months ago
Finished

Automated Parallel Calculation of Collaborative Statistical Models

Large scale statistical data analysis in particle physics

Updated 1 month ago
Finished

iDark

The intelligent Dark Matter survey

Updated 1 month ago
Finished

Real-time detection of neutrinos from the distant Universe

Observing processes that are inaccessible to optical telescopes

Updated 21 months ago
Finished

Giving Pandas a ROOT to Chew on

Modern big data front and backends in the hunt for Dark Matter

Updated 21 months ago
Finished

Related software

swyft

SW

swyft is the official implementation of Truncated Marginal Neural Ratio Estimation, a hyper-efficient, simulation-based inference technique for complex data and expensive simulators.

Updated 17 months ago
20 5