Machine learning models and utilities for exoplanet science.
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Updated
Aug 28, 2020 - Python
Machine learning models and utilities for exoplanet science.
A MLP Neural Network that trains on an exoplanet dataset.
WebApp d'analyse des Exoplanètes afin de développer un outil de ML pour déterminer si une exoplanète est habitable ou non (Projet développé dans le cadre d'un hackathon portant sur l'Espace)
This repository contains submission for the intra-college astronomy hackathon
Use of various machine learning models, including Deep Learning, to classify possible exoplanets from data provided by NASA.
taurex-emcee: automated, parallelized atmospheric retrievals with TauREx 3.1 and the emcee sampler
Checking for false positives in TESS exoplanets searches by centroid calculation in Difference Images from out-of-transit and in-transit fluxes
CI Milestone-2 Project: Interactive Front-end Development
This repository was created as an appendix to my final master thesis: "Detectability and occurrences in CARMENES"
Tools for end-to-end reduction of JWST exoplanet observations
The objective of the project is to apply deep learning techniques in finding the existence of exoplanet. This is achieved by reducing the dimension of the data, followed by generating multiple unique models, parameter tuning of models and finally validating the model.
Analyzed the lightcurve of the star TIC-239332587 and discovered dips in brightness that indicate a planet, which to my knowledge is not listed in any Exoplanet archive.
The official repository for Jux python package
Exoplanet exploration using SVM with different kernels and C values. Dataset can be downloaded from Kaggle website: https://www.kaggle.com/keplersmachines/kepler-labelled-time-series-data
Jupyter Notebooks for the radial velocity tutorials at the Precision Spectroscopy Workshop 2017, held at the Instituto de Astronomia, Geofísica e Ciências Atmosféricas (Universidade de São Paulo) in August 2017.
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