The project aims to leverage machine learning techniques to analyse the flux data and accurately classify stars as either exoplanet-hosting or non-exoplanet-hosting. By training a model on the provided dataset, we seek to uncover patterns and features indicative of exoplanet presence, enabling the model to make predictions on unseen data.

Find out if there are new planets using Deep Learning

Flux contamination analysis (TESS)

A Python package for calculating detection limits for exoplanet high contrast imaging (HCI) datasets.

Predicting potential exoplanets in star systems using light intensity curves data derived from observations made by the NASA Kepler space telescope.

Half-Sibling Regression for High-Contrast Imaging

Matlab code for the adaptive annealed importance sampling based marginal likelihood estimator.

Statistical tools for Combined Analysis of Radial Velocity Signals

The MASER (Magnetically interActing Stars and Exoplanets in the Radio) code developed by Kavanagh & Vedantham (2023).

Direct Exoplanet Imaging with Tensor Decompositions

Algoritmos para o tratamento de curvas de luz, treinamento e utilização de modelos de Machine Learning para a detecção automatizada de exoplanetas.

React + TypeScript web app for AI-powered exoplanet detection. Features NASA-themed UI, 3D planet visualization, interactive light curves, Claude AI chatbot, and real-time predictions. NASA Space Apps Challenge 2025.

PLATO WP36 Exoplanet Analysis System transit detection pipeline (version 1)

PANOTPES Observatory Control Software for a telescope build using a moxa UC-8112A-ME series arm processor.

Full-stack AI system combining physics-based validation and a hybrid CNN-Transformer to classify exoplanets from NASA Kepler light curves.

Code for participation in IIT Kharagpur 2018 inter-iit tech meet competition on exoplanet detection with time-series analysis.

Exoplanet detection prototype built for NASA Space Apps 2025 - combining ML, 3D visualization, and open-source tools.

A culmination of the tasks I will be undertaking as part of a PhD project around the TOLIMAN mission.

Public repository of our works in Exoplanet analysis with Deep Learning

This project performs a comprehensive analysis of exoplanet datasets using Python. It involves preprocessing NASA’s Kepler/K2 mission data, handling missing values, encoding categorical variables, and performing feature selection. EDA reveals correlations between planetary and stellar parameters Random Forest model to enhance predictive accuracy