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 Alexandre Gottel M.Sc.

Alexandre Gottel

M.Sc.

Research associate

Email
gottela@cardiff.ac.uk
Campuses
Room N/2.14, Queen's Buildings, 5 The Parade, Newport Road, Cardiff, CF24 3AA

Overview

I am a researcher in gravitional wave parameter estimation - that is the step of extracting the physical information from observed data.

More specifially, I am looking at the signature of the merging of colliding black holes and neutron start, and trying to decode as much of the physics information that can be found in the data. 

I am particularly interested in Bayesian methods, multi-messenger astronomy, and modernizing the research tools that we use. Working with large amounts of data and people, I also recognise the importance of good coding practices as a necessity and our way forward in research.

Biography

I did my Ph.D. research at the Forschungszentrum Jülich with the RWTH Aachen University. My research was on the detection of solar neutrinos using liquid scintillator detectors. The was directly involved with three experiments: Borexino, a 300-ton detector in Gran Sasso, Italy, JUNO, a 20-kton detector currently under construction in the Guandong province, China, and OSIRIS, a 20-ton pre-detector for JUNO.

After a bachelor and master thesis about cosmic rays and the AMS-02 experiment on the international space station, my PhD expertise on solar neutrinos gave me a lot of additional knowledge not only in hardware but also, and especially, in working with large amounts of data.

My current research at the Gravity Exploration Institute is about using gravitational wave data from LIGO/Virgo in order to infer astrophysical properties. Given my varied cosmic-ray/neutrino background, this new line of research cements my fascination for multi-messenger astrophysics, and my love for Bayesian statistical methods!

My current research is focussed on the inference of black-hole and neutron-star properties from gravitational-wave interferometer data. This is achieved through the use of extensive Bayesian techniques that allow us to make statements about physical properties, while enabling us to quantitatively compare different models.

This work naturally leads me to a shart interest in machine learning for astrophysical inference - as the immense amount of data that will reach our detector in the near future will require nothing short of a revolution in the way that astrophysical inference is performed.

Additionally, I am investigating the possible imprints of dark matter on our gravitational wave data.

Supervision

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