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.

Flux contamination analysis (TESS)

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

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

Find out if there are new planets using Deep Learning

Half-Sibling Regression for High-Contrast Imaging

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

Direct Exoplanet Imaging with Tensor Decompositions

Statistical tools for Combined Analysis of Radial Velocity Signals

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

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.

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

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

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

PlanetFinder is a Python-based tool designed for the detection and analysis of exoplanets using light curve data from the TESS mission. Leveraging the Lightkurve library, it allows users to search for, visualize, and analyze potential exoplanet transits, making it an ideal resource for both citizen scientists and professional astronomers.

Master’s Thesis. Predicting stellar parameters to detect nearby exoplanets using a deep learning approach on billions of data points.

Unsupervised deep learning for exoplanet detection