Recent ground- and space-based surveys have shown that planets between Earth and Neptune in size, known as “super-Earths,” are among the most frequently found planets in the Galaxy. Although the JWST era has provided high-quality atmospheric data on many such super-Earths, modeling tools are crucial for understanding their unobservable interiors. Consequently, interior studies represent the next essential step in gaining a comprehensive understanding of this class of exoplanets. This study investigates the interior structure, thermal evolution, and atmospheric dynamics of the super-Earth GJ 486b using SERPINT, a 1D self-consistent coupled interior structure and evolution model, aiming to understand the planet’s thermal evolution based on an Earth-like structure. Our results indicate that GJ 486b’s core is approximately 1.34 times larger than Earth’s, with a core pressure of about 1171 GPa. The thermal evolution model predicts that the planet’s mantle cools and solidifies over approximately 0.93 Myr. As the magma ocean cools, water is released from the melt, forming a water-rich atmosphere during early solidification. Photolysis of water vapor and subsequent hydrogen escape lead to oxygen accumulation, forming a water- and oxygen-rich secondary atmosphere. Future high-sensitivity JWST observations, with improved wavelength coverage and the detection of additional trace gases, will enable a detailed analysis of the planet’s atmospheric composition, providing crucial insights into the interior, surface, and subsurface properties of GJ 486b.

Interdiffusion within olivine represents an extensively researched phenomenon pivotal for understanding element partitioning and facilitating time-series analysis, particularly in the determination of diffusion coefficients and associated time-series studies. Such investigations find widespread application in geochronological studies. This report begins with an exploration of fundamental principles of Fickian diffusion, followed by the examination of heat diffusion within simplified systems. Subsequently, we undertake modeling of Fe-Mg interdiffusion in forsterite, aiming to ascertain diffusion coefficients through examination of laboratory-grown samples, thereby validating the diffusion model. Employing an Arrhenius fit, we derive activation energies for the olivine samples. Finally, leveraging this model, we compute the total elapsed diffusion time for natural grains sourced from volcanic eruptions, thereby probing the duration between the volcano’s active stage at specific pressure levels and the eruption event.

Galactic magnetohydrodynamics (MHD) involves the study of interaction between magnetic fields and ionized gas within galaxies. In this project, we address the behavior of galactic mean-field dynamos using the mean-field induction equation. We analyze the behavior of generation and shaping of magnetic fields to understand galactic magnetic dynamo in large scale galaxies. Conventionally, the mean field induction equation is solved in terms of the magnetic field components Br, B𝜙, Bz. However, it can also be solved in terms of the magnetic potential. The goal of this project is to solve the mean-field induction equation in the magnetic potential and compare it with the conventional method. We solved the mean-field induction equation in 1D using the Crank-Nicolson method to get the variation of Br, B𝜙, 𝜓 and T, and finally calculated the critical dynamo number for both the cases. We observed that the initial seed field decays for dynamo numbers less than the critical dynamo number and grows for dynamo numbers greater than the critical dynamo number. We also found that the potential term 𝜓 splits into positive and negative lobes irrespective of the seed field.

With the advancements of observation techniques, many Earth-like exoplanets have been discovered. But, these techniques can only probe exoplanetary atmospheres and thus, the mechanisms of the interior are difficult to quantify. Studying time evolution of interiors of these exoplanets might give us a better understanding of how planets like Earth formed. We present a machine-learning based approach to investigate and predict time-evolution of interiors of rocky exoplanets using neural networks (NN).

This directory contains the codes, methods and assignments taught in course P346 Computational Physics fall 2021 and P452 Computational Physics spring 2024 in NISER Bhubaneswar. The codes require minimum use of python libraries to serve the purpose of basic start to computational methods in physics and maths. To run the codes, you require the file "CPL_Library.ipynb". This file contains all the functions required code bits to execute the assignments. Importing may require different system requirements. This assignment package uses inline run for the library functions.

Clouds have a significant role in meteorological studies. They form the basis of the mechanism for precipitation; they contribute a massive portion of the earth’s albedo and significantly affect the wind, humidity, temperature, weather, and even the climate of any region. Therefore, the study of clouds becomes essential. This study aims to study the cloud base height, i.e., the lowest altitude from where the clouds begin to get detected, and the precipitation variation with the cloud base height within a duration of monsoon period of three consecutive years, namely June, July, August and September of 2019, 2020, 2021. The studies were extensively made using the ground-based instruments present at the Physical Research Laboratory in Ahmedabad, India, which includes the Vaisala ceilometer for measurement of cloud base height and parsivel disdrometer for measurement of precipitation. Along with this, the reanalysis data from ERA5 was also used for comparing the outcomes and validating the results. The choice of this duration provides a big picture of the impact of a reduction in human activities on the lower atmosphere. The cloud coverage was nearly the same during the entire duration except for a tiny dip in the cloud percentage. However, a significant dip was noted in the precipitation occurrences and the amount of precipitation. We explain these dips due to the reduction in cloud condensation nuclei (CCN) as covid restrictions were imposed. This study, in association with satellite and forecast data, will serve as a comprehensive basis for understanding the weather in the western part of India.