About Me
Iām Krishnanjan Sil, an aspiring theoretical physicist captivated by the universeās deepest mysteries. From gazing at stars as a child to solving complex equations today, my journey is driven by a passion for quantum mechanics, cosmology, and particle physics. I aim to bridge theoretical models with empirical discoveries, pushing the boundaries of our cosmic understanding. When not immersed in research, I capture the night sky through astrophotography, dive into sci-fi novels, or hike to reflect on natureās grandeur.
My Life
Why Physics?
Physics is the foundation of reality, a discipline that decodes the universeās laws from quarks to quasars. My fascination began with a childhood telescope, revealing Jupiterās moons and sparking a lifelong quest to understand cosmic phenomena. Physics blends mathematical elegance with empirical rigor, offering answers to profound questions about existence. My mission is to contribute to theories that reshape our view of the cosmos.
Research Experience
Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata, India
Research Intern, January 2025 ā Present
Working under advisory of Prof. Supratik Pal and research scholar Rahul Shah at the Indian Statistical Institute, Kolkata, to reconstruct the Hubble parameter using mock data from future gravitational-wave missions. Applied Gaussian Process Regression from scratch to reconstruct the Hubble parameter and experimented with Artificial Neural Networks to model luminosity distanceāredshift relations. Conducted performance analysis of the models, addressing computational limitations and exploring alternatives like Physics-Informed Neural Networks. Authored a dissertation and gained valuable experience in data-driven cosmology and scientific computing.
Prof. Maria Giovanna Dainotti's Group, National Astronomical Observatory of Japan
Research Intern, March 2025 ā Present
Worked under Prof. Maria Giovanna Dainotti's group on Gamma-Ray Burst (GRB) light curve reconstruction, co-authoring a preprint (arXiv:2506.23681v1). Co-developed the Temporal Convolutional Network (TCN) model for GRB light curve fitting and contributed to the performance analysis of both TCN and Deep Gaussian Process models. Assisted in result computation and interpretation. Currently contributing to follow-up studies extending this work toward improved regression techniques and cosmological applications.
Research Interests
My research lies at the intersection of theoretical physics and observational data, aiming to unravel the universeās fundamental mysteries through innovative approaches.
- Quantum Mechanics: I explore quantum entanglement and quantum information theory, simulating multi-qubit systems to study Bell state correlations. My work aims to advance quantum computing and understand decoherence in complex quantum systems.
- Cosmology: Focused on cosmic inflation and dark energy, I analyze CMB data to constrain inflationary parameters and investigate baryon acoustic oscillations to understand the universeās early evolution.
- Particle Physics: Using Monte Carlo simulations, I model particle collisions to probe new physics, with a focus on neutrino oscillations and matter-antimatter asymmetry.
- General Relativity: I analyze LIGO gravitational wave data to study black hole dynamics and explore modified gravity theories to explain cosmic acceleration without dark energy.
Projects
Non-Parametric Reconstruction of the Hubble Parameter with LISA using Gaussian Process Regression
2025
As a research intern at the Indian Statistical Institute, Kolkata, I worked on reconstructing the Hubble parameter from simulated gravitational-wave data. I developed pipelines to generate mock luminosity distanceāredshift data for next-generation observatories like LISA and Einstein Telescope. Leveraging Gaussian Process Regression (GPR), I reconstructed continuous H(z) curves from sparse data and evaluated the limitations of kernel-based approaches. To address computational bottlenecks, I mentioned alternative algorithms. My work has been portrayed in my dissertion which is available when asked. Currently, exploring better models for robust cosmological modeling.
View GitHub RepositoryMulti-Model Framework for Reconstructing Gamma-Ray Burst Light Curves
2025
Under the mentorship of Prof. Maria Giovanna Dainotti, I contributed to the development of a machine learning framework for modeling the light curves of Gamma-Ray Bursts (GRBs), with the aim of uncovering statistical correlations relevant for cosmology. I co-developed and fine-tuned the Temporal Convolutional Network (TCN) architecture to capture temporal dependencies in GRB afterglow data, enabling accurate interpolation and feature extraction from irregular time series. I was also involved in the analysis of TCN and Deep GP models, and result calculation. This work culminated in a co-authored preprint (arXiv:2506.23681v1) and has led to follow-up projects involving better deep learning architectures and scaling methods for large GRB datasets.
View PreprintPapers & Publications
Explore my publications on Google Scholar.
Education
High School, Indian School Certificate (ISC)
Sri Aurobindo Institute of Education, India, āMarch 2024
Graduated in Science, with a strong foundation in Physics, Chemistry, Mathematics, and Biology. Medium of education and compulsory subject: English
B.Sc. in Physics
Ramakrishna Mission Vivekananda Centenary College, India, July 2024āJune 2028 (expected)
Pursuing a 4-year Bachelor of Science (Honors with Research) degree in Physics. Currently, in semester 3."
Hobbies
Science Fiction Reading: Inspired by the imaginative worlds of Isaac Asimov and Arthur C. Clarke.
Coding: Developing simulations and visualizations to enhance physics research.
CV
Download my detailed CV to explore my academic and research journey.
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Letās connect to discuss physics, research collaborations, or innovative ideas!
Email: 1krishnanjansil1@gmail.com