Research Activities

I have been associated with the Belle and Belle II experiments since 2011. My doctoral research focused on bottomonium studies using Belle Υ(2S) data. Following my PhD, I expanded my scope to rare B decays, specifically investigating lepton-flavor-violating decays and precision measurements of B → K^*ℓℓ and B → Kℓℓ.

Currently, our group is working on the measurements of B → K^*τ⁺τ^- and radiative rare decays of the B meson.

On the instrumentation front, I contributed to the development and commissioning of a particle identification detector for Belle II, which relies on imaging time-of-propagation (iTOP) measurements to distinguish between pions and kaons with high precision.

I recently joined the CMS experiment at the Large Hadron Collider (LHC). My primary hardware contribution is towards the High Granularity Calorimeter (HGCAL) upgrade activities, which is crucial for the High-Luminosity LHC era.

In terms of physics analysis, we intend to contribute to the B-Physics program of CMS. We aim to study b-hadrons such as B_s, B_c, and Λ_b. These states are largely inaccessible at B-factories (like Belle II) due to kinematic constraints. Ultimately, the CMS B-physics program aspires to complement and independently confirm the findings of B-factories, providing a complete picture of flavor physics.

Recent discrepancies between the Standard Model (SM) prediction of the anomalous magnetic moment of the muon, a_μ^SM, and experimental measurements, a_μ^exp, suggest the possibility of new physics. The proposed J-PARC Muon g-2/EDM experiment aims to measure the muon's anomalous magnetic moment with a precision of 460 ppb (systematic + statistical) using reaccelerated ultracold muons injected into a magnetic storage ring.

A crucial aspect of this experiment is the accurate reconstruction of positron tracks from muon decays, currently performed using the Hough transformation technique. However, the current track-finding process is computationally intensive, and a speedup factor of 10 is required for efficient data processing.

To address this challenge, we are developing alternative strategies utilizing Graphical Processing Units (GPUs) and Graph Neural Networks (GNNs) to accelerate track reconstruction without compromising accuracy.