Dr. Rachik Soualah is a particle physicist. He received his Ph.D. from the University of Heidelberg in Germany. He worked as a postdoctoral fellow at the International Center for Theoretical Physics (ICTP) and INFN in Trieste (Italy) and the European Organization for Nuclear and Particle Research (CERN) in Geneva (Switzerland) with the ATLAS collaboration, the world's largest and most complex scientific experiment, at the CERN-Large Hadron Collider (LHC). Prior to joining Khalifa University, he was a faculty member at the University of Sharjah. Dr. Soualah is currently a member of the International Computing Board at the CERN-ATLAS collaboration and was a National Contact Physicist and a member of the Upgrade Advisory Board.
His research interests include probing the fundamental structure of the universe by investigating the elementary particles and their behavior. His work particularly focuses on well-motivated physics Beyond the Standard Model (BSM) theories, which incorporate various aspects of Higgs and top quark physics and Dark Matter searches at existing and future colliders such as the LHC and Future Circular Collider (FCC).
He has received numerous international awards and secured several competitive research grants (from Germany-Norway, Italy-Switzerland, and the UAE), and is also a member of many international physics communities. During his time in the UAE, Dr. Soualah successfully integrated the ATLAS collaboration into the UAE scientific community and served as the inaugural leader of the UAE-ATLAS cluster for three years. He has been actively promoting particle physics in the education sector, as well as a cutting-edge research direction in the UAE.
Physics Beyond the Standard Model:
The Standard Model has shown remarkable success in describing physics at the electroweak scale and below. Over a decade ago, the major discovery of the Higgs boson at the Large Hadron Collider, combined with the subsequent improvements in understanding its properties, allowed physicists to make significant progress in our understanding of the universe.
However, despite its remarkable success in explaining most of the observations in Nature, the Standard Model (SM) has not been able to address several unresolved questions like the number of quark and lepton families, the hierarchy between the Electroweak and Planck scales, the origin of neutrino mass and mixing, the strong CP-problem, and other unresolved problems in cosmology and Astrophysics such as the nature of Dark Matter (DM), and several other issues. This motivates strongly the presence of new physics Beyond the Standard Model (BSM).
With the ATLAS, FCC and other research groups, we are investigating how new physics of well-motivated models beyond the Standard Model could potentially be discovered via various channels by analyzing data from the LHC as well as simulated events of FCC-ee collider. The main research topics in this project include:
- Top quark physics
- Higgs Physics
- Dark Matter Searches in the neutrino sector at colliders
- Axions searches at FCC-ee
(Image source: ATLAS Experiment database © 2024 CERN).
Computing and Software: Application of Artificial Intelligence (AI) and Machine Learning (ML) in High Energy Physics (HEP):
The field of High Energy Physics (HEP) heavily relies on software and computing (S&C) for both experimental and theoretical work. Experimental design, data acquisition, instrumental control, event reconstruction, and analysis all heavily depend on S&C, which has grown in size and complexity to match that of experimental instruments. The accuracy of theoretical calculations and simulations is often driven in large part by S&C as well. Over the past decade, S&C has had remarkable success in this pivotal role within HEP. Additionally, the Big Data recorded with the ATLAS detector at LHC requires sophisticated computing techniques such as applying AI and ML in High Energy Physics. In this project, we are trying to develop new approaches to identify the elementary particles with high accuracy and events in proton-proton collisions, as well as enhance the data reconstruction and make better trigger decisions.
These studies include:
(Image source: ATLAS Experiment database © 2024 CERN).
