Ciela is a scientific research institute associated with the Faculty of Arts and Science of Université de Montréal. The institute’s mission is to contribute to breakthrough discoveries in astrophysics and cosmology by developing innovative data analysis and machine learning methods. It also aims to use unsolved and challenging data analysis problems in astrophysics to push the boundaries of machine learning and to make significant advances that can contribute to the successful and widespread use of machine learning in other fields of science.
In the past few decades, several significant breakthroughs have been made in astrophysics, including the discovery of the accelerated expansion of the Universe, the detailed mapping of the anisotropies of the cosmic microwave background, and the detection of gravitational waves from binary black hole systems.
Today, there exist several key open questions in astrophysics whose answers can revolutionize our understanding of our Universe. Among these are the enigmatic nature of dark matter and dark energy, the birth of the first stars, the formation of supermassive black holes, and the tantalizing possibility of the existence of life on other planets outside the solar system.
To tackle these fundamental questions, new generations of observatories are being constructed, which will generate unprecedented volumes of data. This wealth of information offers a unique opportunity to potentially find definitive answers to some of the most profound questions about the nature of the Universe.
In particular, the fields of astrophysics and cosmology are now entering a new era of large surveys and challenging data analysis problems. Groundbreaking experiments such as the Legacy Survey of Space and Time (LSST) at The Vera C. Rubin Observatory will be mapping the entire southern sky every three nights, producing unprecedented maps of the transient sky over its 10-year planned mission. In parallel, several other facilities, including the Euclid space telescope, are set to conduct similar comprehensive surveys of the entire sky. The Square Kilometre Array (SKA) with an astonishing total collecting area of approximately one square kilometer will be able to survey the sky more than ten thousand times faster than before, requiring exascale computing to process the vast amounts of incoming data.
Achieving the scientific goals of these experiments and uncovering the answers to the most fundamental questions in modern astrophysics is only possible with the analysis of these colossal volumes of data. Traditional statistical models, commonly employed in astrophysics, are often unfeasible and lack the required accuracy to tackle these challenging problems.
Thanks to several major advancements in the field of artificial intelligence, over the past decade, machine learning has emerged as a transformative paradigm for data analysis. Machine learning has been shown to be able to address the complexities of astrophysical data analysis. There is no doubt that the next generation of major discoveries in astrophysics will be enabled by machine learning.
The mission of Ciela Institute is to lead and contribute to breakthrough discoveries in astrophysics by focusing on and developing numerical and computational methods based on machine learning.