« Hopes and Challenges in Information Science for Cosmology » |
Florent Leclercq |
Surveys of the cosmic large-scale structure carry opportunities for building and testing cosmological theories about the origin and evolution of the Universe. Answering such physical questions requires extracting information from large astronomical data sets with sufficient accuracy and precision. While the next generation of surveys soon starting will generate orders of magnitude more data than previously, it is becoming increasingly clear that traditional techniques are not up to the challenge of fully exploiting the raw data. With next-generation experiments, advancing the research frontier will require solving challenging and unique statistical problems, to unlock the information content of massive and complex data streams.
The last decade has seen vast progress in the field of information science, with routine large-scale applications of machine learning. Many of these developments have been driven by (astro-)physics and by the evolution of computing hardware. However, the story started much earlier with an epistemological controversy: how do we analyse evidence and change our minds as we get new information? How do we make rational decisions in the face of uncertainty? I will review some of these historical aspects and discuss some past successes and future promises of information science for cosmology, in applications such as speeding up models, going beyond approximations, finding the information content, and dealing with complex inference tasks. Throughout the talk, I will present the outcomes of some recent methodological advances. I will then turn to the future and highlight some opportunities and challenges for the field. |
vendredi 21 octobre 2022 - 11:00 Amphithéâtre Henri Mineur, Institut d'Astrophysique de Paris |
Page web du séminaire / Seminar's webpage |