Personlig profil

Forskning

I am part of the SMARTHEP training network funded by the Marie Skłodowska-Curie Actions grant by the EU. The focus of the network is to improve real-time analysis in high energy physics and industry. My current research project related to this is to improve part of the track calibration in the Time Projection Chamber at the ALICE experiment at the LHC. 

When collisions happens inside the detector there is a build-up of space charge in the gas volume of the TPC. This build-up creates distortions that change the parameters of the tracks that requires a corrections. Furthermore, due to the large amount of data expected to be collected during Run 3, traditional algorithms have issues processing the high data input. My research is to try and correct these space-charge distortions using Machine Learning.

I also do physics analysis in the area of high energy physics. More specifically, in ALICE we study heavy-ion collisions, where we try to study the primordial state of matter known as the Quark Gluon Plasma (QGP). My studies are related to the phenomenon known as collectivity where we study the anisotropic expansion of the medium. Different shapes can emerge from the result of two colliding nuclei, and by studying these shapes, we can try to constrain the initial conditions which provides important information about the key transport parameters (such as shear and bulk viscosity) that models use to describe the evolution of the system. 

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