Short introduction of myself:
I am a PhD research scholar in the Department of Mathematics at Presidency University, Kolkata, working under the guidance of Dr. Supriya Pan. My research focuses on observational cosmology, where I employ both parametric and non-parametric methodologies to explore the underlying fabric of the Universe. With a strong emphasis on data-driven techniques, I work to reconstruct the cosmic expansion history and investigate the role of dark energy and other fundamental components influencing the Universe’s large-scale dynamics. My goal is to provide deeper insights into the phenomenon of late-time acceleration and explore possible deviations from the standard ΛCDM cosmological model.
Outside the academic realm, I’m someone who thrives on creativity, movement, and laughter. I actively engage in sports, which keeps me energetic and grounded, and I enjoy spending weekends at the cinema with friends, savoring everything from thought-provoking dramas to light-hearted comedies. In my free time, I like creating digital content, which offers me a fun outlet to blend storytelling with imagination. I'm also deeply inspired by legendary comedians like Charlie Chaplin and Mr. Bean, whose expressive humor and timeless performances have nurtured my love for acting and comedy. I believe this balance between scientific rigor and artistic expression shapes my unique perspective on both life and research.
Research Domain: Observational Cosmology:
My research sits at the intersection of theoretical physics and astronomical observations, rooted in the ever-evolving field of observational cosmology. This discipline seeks to understand the origin, structure, dynamics, and ultimate fate of the Universe by connecting data from distant galaxies, cosmic microwave background radiation, and large-scale structures with robust theoretical frameworks.
Methodological Framework:
In my research, I use a combination of parametric and non-parametric methods to study how the universe has expanded over time. Parametric models help test specific theories, while non-parametric methods are more flexible and allow the data to speak for itself without assuming a fixed model.
To make the most of non-parametric approaches, I work with modern machine learning tools. I often use Gaussian Processes with libraries like Gapp, tinygp, GPyTorch, JAX, and NumPyro to model patterns in the data. I also experiment with Artificial Neural Networks (ANNs) to uncover complex features.
For deeper cosmological studies, I rely on powerful software packages like CLASS (for modeling the Universe), MontePython, Cobaya, Colfi (for parameter estimation and statistical testing), and emcee (for sampling). I'm always learning more about how these tools work under the hood and enjoy discovering new ones that might improve my research.
From the parametric perspective, I use specific cosmological models, such as ΛCDM and its extensions and estimate key parameters like the Hubble constant, matter density, and dark energy equation of state. Using Bayesian inference techniques, I determine the best-fit values for these parameters and quantify how well different models agree with the data. I’m continuously learning how these packages work under the surface and enjoy exploring new tools that can make this process more accurate and efficient.
Focus Areas:
Dark Energy and Cosmic Acceleration: Understanding the mysterious force behind the accelerated expansion of the Universe through reconstruction of the Hubble parameter and deceleration parameter.
Model-Independent Reconstructions: Utilizing tools such as Gaussian processes and machine learning to extract signals from data without assuming specific cosmological models.
Scientific Contribution:
By blending rigorous mathematical treatment with the richness of contemporary observational datasets (like SNe Ia, DESI-BAO, CMB, and H(z)), my work aims to:
Identify potential deviations from the standard ΛCDM model.
Test the viability of alternative cosmological frameworks, and
Offer fresh insights into one of the most profound questions in physics: what governs the fate of our universe?
General theory of relativity
Inspiration
motivation