Veronika Chobanova is a particle physicist. She obtained her undergraduate degree at the Technical University of Munich. Veronika did her doctorate at the Max Planck Institute for Physics and was conferred a PhD at the Ludwig Maximilian University of Munich in 2015. For her thesis, she measured fundamental particles properties using data of the Belle collider experiment in Japan and obtained world’s leading results.
Veronika joined the University of Santiago de Compostela and IGFAE in 2015 as a postdoctoral researcher. Since then, she has been a member of the LHCb experiment at the Large Hadron Collider (CERN). LHCb is a collaboration of 1500 members where Veronika has held important positions of responsibility to lead international teams of researchers. She leads one of LHCb’s flagship measurements, that of a phase called phis, where she has produced the world’s most precise results. In addition, she develops novel methods based on deep neural networks to boost the precision of this and other important LHCb physics results.
Besides the Ramón y Cajal grant, Veronika has obtained multiple other grants and awards, including a CERN Corresponding Associateship, a Juan de la Cierva – incorporación (2017), and a Discovery Early Career Award (2020, Australia). She has multiple publications, almost exclusively in high-profile journals. Veronika is a peer-reviewer for some of the top journals in her area and reviews grant applications for the Australian Research Council and FONCYT (Argentina). She has organized major international conferences with hundreds of participants.
She is a member of the LHCb experiment at the Large Hadron Collider (CERN) where she has held important positions of responsibility to lead international teams of researchers.
The goals of LHCb are to find previously undiscovered particles or forces of nature by performing high-precision measurements. Veronika has been leading LHCb’s flagship measurement of a phase called phis where she has produced the world’s most accurate results in the past. In this project, she will develop novel techniques for this particularly complex analysis to further improve its accuracy. She will focus on flavor labeling, which is a crucial ingredient for this and other important LHCb measurements. Veronika will study a new approach, inclusive flavor labeling, which is based on deep neural networks and aims to exploit the data optimally. This promises not only to increase the chances of finding previously unknown physics effects in phis and related measurements, but will also open up new possibilities for expanding the LHCb physics program.