Shirsendu pursued a B.Sc. in Physics from the University of Calcutta, India, and subsequently completed an M.Sc. in Physics in July 2023 from the Indian Institute of Technology (IIT), Madras, India.
During his M.Sc. thesis work, he focused on heavy flavour Ds meson reconstruction in proton-proton collisions at CMS using Run-2 CMS data. He calculated the raw yields and width of the invariant mass peak of Ds candidates for different transverse momentum ranges, utilising the Boosted Decision Tree technique based on machine learning algorithms.
After completing his Master's, Shirsendu worked as a project associate for nine months at IIT Bombay, India. In this position, he investigated the phenomenological topic of variation in R2 and P2 correlation functions for identified particles produced in proton-proton collisions across different simulation models.
Within LIV.INNO, he will explore axion-like particles (ALPs) using ultra-peripheral Pb-Pb collision data recorded with the ATLAS detector at the LHC. This work will require various analysis techniques combined with forthcoming LHC heavy ion datasets, enabling us to achieve the best possible sensitivity to ALPs, potentially leading to a discovery or establishing the most stringent upper limit on their production.