Du machine learning dans l’espace

Clefs CEA n°69 – L’intelligence Artificielle, Novembre 2019.

En astrophysique, à l’instar de nombreux autres domaines scientifiques, le machine learning est devenu incontournable ces dernières années, et pour un très large éventail de problèmes: restauration d’images, classification et caractérisation des étoiles ou des galaxies, séparation automatique des étoiles des galaxies dans les images, simulation numérique d’observations ou de distribution de matière dans l’Univers…

DEDALE: Mathematical Tools to Help Navigate the Big Data Maze

Managing the huge volumes and varying streams of Big Data digital information presents formidable analytical challenges to anyone wanting to make sense of it. Consider the mapping of space, where scientists collect, process and transmit giga-scale data sets to generate accurate visual representations of millions of galaxies. Or consider the vast information being generated by genomics and bioinformatics as genomes are mapped and new drugs discovered. And soon the Internet of Things will bring millions of interconnected information-sensing and transmitting devices.

Big Bang and Big Data

The new international projects, such as the Euclid space telescope, are ushering in the era of Big Data for cosmologists. Our questions about dark matter and dark energy, which on their own account for 95% of the content of our Universe, throw up new algorithmic, computational and theoretical challenges. The fourth concerns reproducible research, a fundamental concept for the verification and credibility of the published results.

Astrophysique et IRM, un mariage qui a du sens

La Direction de la recherche fondamentale au CEA lance le projet COSMIC, né du rapprochement de deux compétences en traitement des données localisées à l’Institut des sciences du vivant Frédéric-Joliot (NeuroSpin) et au CEA-Irfu (CosmoStat). Les mécanismes d’acquisition de données en radio-astronomie et en IRM présentent des similarités. Les modèles mathématiques utilisés sont en effet basés sur les principes de parcimonie et d’acquisition comprimée, dérivés de l’analyse harmonique.

DEDALE Provides Analysis Methods to Find the Right Data

​A key challenge in cosmological research is how to extract the most important information from satellite imagery and radio signals. The difficulty lies in the systematic processing of extremely noisy data for studying how stars and galaxies evolve through time. This is critical for astrophysicists in their effort to gain insights into cosmological processes such as the characterisation of dark matter in the Universe. Helping scientists find their way through this data maze is DEDALE, an interdisciplinary project that intends to develop the next generation of data analysis methods for the new era of big data in astrophysics and compressed sensing.