Research

Heavy Flavour Physics:

Heavy flavour physics is a field of study that focuses on the behaviour and properties of particles containing heavy quarks, such as charm and bottom quarks. The study of heavy flavor production in heavy-ion and hadronic collisions important in understanding the properties of the hot and dense matter created in these collisions. Studies in heavy flavour production provides insight into understanding interesting phenomena such as heavy quark energy loss  and collective behavior within the QGP medium. These studies also provide stringent tests to perturbative QCD calculations which are fundamental to understand the dynamics and production mechanisms of heavy flavour in heavy ion and hadronic collisions.

QGP Phenomenology:

QGP (Quark-Gluon Plasma) Phenomenology is the study of the properties and behavior of the quark-gluon plasma, a state of matter that is formed in high-energy heavy-ion collisions. This field of research focuses on understanding the various observable features of the quark-gluon plasma, such as its equation of state, transport properties, and particle production patterns. QGP Phenomenology also involves comparing experimental data with theoretical models to gain insights into the fundamental properties of the strong nuclear force and the behavior of matter at extreme temperatures and densities. 

Exotics:

Exotic particles in particle physics refer to hypothetical particles that are not predicted by the Standard Model of particle physics, such as particles with more than three quarks or particles with fractional electric charge. Research in this field involves searching for evidence of these particles through experiments at particle accelerators, as well as developing new theoretical models to explain their properties and interactions. Current research in exotics is focused on understanding the nature of dark matter, investigating the properties of potential new particles, and exploring the limits of the Standard Model of particle physics. 

Detector Simulation:

Detector simulation is a computational technique used in particle physics research to simulate the behavior of particles as they interact with detectors. This involves modeling the interactions of particles with matter, as well as the electronic response of the detectors. Detector simulation is an essential tool for designing new detectors and optimizing their performance, as well as for analyzing the data collected in experiments. By comparing the simulated data with experimental data, researchers can test theoretical models, search for new physics, and better understand the properties of particles and their interactions. 

Machine Learning:

Machine learning is a powerful tool used in particle physics research to analyze large and complex datasets generated from particle collider experiments. It involves developing algorithms and models that can automatically identify patterns, classify particles, and distinguish signal from background. Machine learning is used for a variety of applications in particle physics, such as event reconstruction, particle identification, anomaly detection, and optimization of data selection criteria. The use of machine learning has the potential to significantly enhance our understanding of fundamental particles and interactions, as well as to discover new physics beyond the Standard Model. 

Quantum Computing: 

Quantum computing is an emerging technology that uses quantum mechanics to process information in ways that are fundamentally different from classical computing. It has the potential to revolutionize high-energy physics by enabling more efficient simulations of complex quantum systems, such as the behavior of subatomic particles. Quantum computing can also be used to solve optimization problems and to improve the analysis of large datasets generated by particle collider experiments. Additionally, quantum computing may lead to the discovery of new algorithms and computational techniques that can aid in our understanding of fundamental physics.