Florent Renaud

Biography

Florent Renaud is an astrophysicist at Lund University in Sweden, and will join the Strasbourg astronomical observatory (ObAS) as research director at the end of 2023, where he will be welcomed by Dr. Benoît Famaey.

His work focuses on the formation of galaxies, in particular on the different phases of galaxy evolution since the Big Bang, as traced by stellar populations. He designs and runs large galaxy and cosmological simulations, which he then uses to help interpreting the observational data at all wavelengths.

Born and raised in Vendée (Western France), Florent Renaud first came to Strasbourg in 2004 to study engineering, but quickly discovered he was more passionate about fundamental sciences, particularly astronomy. Thus, he took the astronomy master courses offered at the astronomical observatory, in parallel with his engineering studies.

After a 6-month internship with NASA at Caltech (United States), he obtained his engineering degree and master in astronomy in 2007. He then obtained his PhD in 2010 as a joint degree from the universities of Vienna (Austria) and Strasbourg. During his first post-doctoral position in Saclay (France), he became familiar with high performance computing and astrophysical hydrodynamics. He then broadened his perspectives at the University of Surrey (United Kingdom), exploring stellar dynamics, before moving to Sweden in 2017, where he combines this knowledge into his present work. In 2020, he became director of the master programme in astrophysics at Lund University, with the aim to offer the next generations the same inspiring experience from which he benefited as a student.

Florent Renaud loves touring the world to talk about astronomy in seminars and conferences. He always brings his camera, ready for his next adventure at the bottom of a deep canyon, on top of a volcano or a glacier, or gazing at the Northern Lights.

Fellowship 2023

Dates - 01/09/2023-31/12/2024

Project summary

DECODING DARK MATTER USING THE MILKY WAY'S STELLAR STREAMS

Dark matter is the most abundant constituent of the Universe, and a crucial ingredient in the assembly of galaxies. Yet, its nature remains unknown. All past attempts to detect dark matter particles have only managed to rule out hypotheses, without ever reaching positive conclusions. In this context, alternative theories have emerged, replacing dark matter with a modification of the equation of gravitation to yield the same effect. However, these ideas also face difficulties in building a self-consistent framework compatible with observations. This long-lasting situation calls for more astrophysical constraints on the nature of dark matter.

A major difficulty in this field is that dark matter dominates in the galactic halos, which are almost entirely devoid of luminous probes. Stellar streams are the exception. Streams are long tails made of the stars escaping from the star clusters orbiting our galaxy. Their shape, length, and structure are highly sensitive to the distribution of dark matter around them. Studying how they form, and what shapes them, provides unique information on fundamental properties of the halos, like their shape, their mass profiles, and their clumpiness. These constitute important constraints on the nature of dark matter, or on the properties of the alternative theories of gravitation. However, this problem is highly degenerated: structures along the streams could equally well be caused by dark matter, or variations in the internal evolution of their progenitors. Measuring this accurately requires that stream simulations capture both the internal dynamics of clusters and the galactic context in which they evolve. This represents a huge range of scales to be treated simultaneously, which cannot be done with conventional numerical methods due to the enormous cost in computing time.

The goal of this project is to develop an innovative method to model star clusters and their streams at high precision, and much faster than existing tools. This will be achieved by reducing the integration of internal dynamics to a handful of differential equations. With acceleration factors of the order of several tens of millions compared to current methods, it will become possible to run a very large number of simulations, and explore the parameter space in depth, to establish which setups best match the observations. We will then learn which properties of dark matter are compatible or incompatible with observations of the Milky Way, as conducted by the world-renowned team at the Strasbourg astronomical observatory.