Abhishek

PhD Student | University of Tennessee Knoxville

First-year PhD student exploring high-energy and particle physics, with a growing focus on deep learning for detector optimization and particle identification.

About Me

I am a first-year PhD student at University of Tennessee, Knoxville (UTK). I did Integrated Master of Science in Physics from the National Institute of Science Education and Research (NISER), Bhubaneswar. My academic and research journey is rooted in experimental high energy physics, with a focus on studying detector optmization, reconstruction algorithms in high-energy collisions and developing advanced detector technologies.

I have hands-on experience with simulation tools like GEANT4 and Pythia, and I designed and applied suitable deep learning models to enhance particle discrimination and data analysis. My master's thesis involved both simulation and experimental work, focusing on track reconstruction algorithms and building Resistive Plate Chambers for Muon Scattering Tomography. I am always looking for new opportunities in relevant fields.

Teaching

PHYS231: Electromagnetism Lab

I am currently a teaching assistant for PHYS231 Electromagnetism Lab at the University of Tennessee. In this role, I guide students through hands-on experiments and help them understand key concepts in electromagnetism.

Lab Manuals

Education

Aug 2025 - Present

Doctoral Cand, Early Stage - Physics (GPA: 4.00/4.00)

University of Tennessee, Knoxville,(TN) USA

July 2019 - July 2024

Integrated M.Sc Physics (CGPA 8.36/10)

National Institute of Science Education and Research (NISER), Bhubaneshwar, India

2018

Senior-Secondary School (Class-12th)

Central Board of Secondary Education (CBSE), Delhi, India

Research Projects

Muography Project Image

Developing Imaging Algorithms and RPCs for Muography Studies

Master's thesis focusing on track reconstruction algorithms (POCA, BCA) and construction of Resistive Plate Chambers (RPCs) for Muon Scattering Tomography.

Cover Page

Classification of hadrons in Granular Hadron Calorimeters

Using a deep learning model (Deep Sets) to improve discrimination power among protons, pions, and kaons by increasing hadron calorimeter granularity.

rho_meson_invariant_mass

Investigation of ρ⁰(770) Meson Production

Studying the production of ρ⁰(770) mesons in pp collisions at √s=13.6 TeV using Pythia-generated events and invariant mass reconstruction.

Publications & Contributions

  • First Author: "A modular muon telescope for tomography and radiography applications," Nuclear Inst. and Methods in Physics Research, A. [Publication]
  • Co-Author: "Hadron Identification Prospects With Granular Calorimeters, MDPI Particles" . [Publication]
  • Co-Author: "Neuromorphic Readout for Homogeneous Hadron Calorimeters," MDPI Particles. [Publication]
  • Co-Author: "End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters" MDPI Particles. [Publication]
  • Presenter: "Particle Identification in Highly Granular Calorimeters With Deep Sets" at Fifth MODE workshop Crete (Greece). [Talk]

Technical Skills

Data Analysis

ROOT Python C++ Pandas NumPy Uproot SciPy

Simulation & Machine Learning

GEANT4 Pythia JAX PyTorch Graph Neural Networks DeepSets

Tools & Hardware

Git JupyterLab Linux LaTeX ASIC FPGA

Get In Touch

I'm currently open to new opportunities. Feel free to reach out!

Say Hello