About Me

Bio

I’m Adrien Anthore, an astrophysicist and amateur astronomer. I am curently study engineer at Observatoire Astronomique de Strasbourg working with Laurent Chemin.
I graduated from the Physics Intensive Track at the Sorbonne University (2022), SPRINT (Sorbonne Physics Research Intensive and New Technologies), and from the Astrophysics Graduate Programme at the Paris Observatory - PSL (2024), in IRT (International Research Track), specialising in Observational Astrophysics.

Since 2021, I have been actively involved in science communication and popularisation. I am committed to sharing my knowledge and raising public awareness of the importance of scientific research and the challenges it faces. It is in this spirit that I have created this programme: L’Observatoire d’Adrien, a programme for popularising astrophysical topics on the Internet. In addition to my online activities, I regularly participate as a speaker in scientific congresses or festivals open to the general public, such as the Explor’Espace festival in 2021 or the “Fête de la Science” in universities since 2022. I also intervene in the school environment, mainly to discuss scientific studies and career prospects.

As a child I discovered astronomy through books and by observing the stars at night. I wanted to continue observing, first with binoculars to look at the moon and the planets. Then I started buying instruments to observe more and more objects. Today I have a 200/1200 Dobson, a 70/700 refractor and an electronically assisted instrument. The images I take are mostly from my EA, but also from Lucky Imaging. I also make observational drawings.

If you are interested in my work and would like me to participate in an event you are organising, whether for school or other purposes, please contact me with details of your project. I will be happy to help and offer my services. Send an email here

Research interests

Galaxy: general - Radio lines: galaxies - Radio continuum: galaxies - Instrumentation: interferometers - Methods: observational - Methods: data analysis - Deep Learning.