General Toolkit for Modeling Radial Velocities
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Updated
Jul 28, 2017 - Jupyter Notebook
General Toolkit for Modeling Radial Velocities
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.
A pure Python code for working with radial velocities of stars with a massive companion.
Tools for data-driven spectra models with Gaussian processes. Pronounced "soap."
The use of Gaussian processes in the analysis of radial velocity measurements if you are into those things.
Simple code to compute radial velocities of an astronomical binary system (e.g. an exoplanet orbiting a star) based on its orbital parameters.
A not so mini implementation of the Gaussian process frameworks described on Rajpaul et al. (2015) and Jones et al. (2017).
This repository was created as an appendix to my final master thesis: "Detectability and occurrences in CARMENES"
Plotting functions/scripts for use with RvSpectML
Repo for RvSpectML code still in the experimental stage
README for RvSpectML project
Decompose spectrum of resolved SB2 binary to extract radial velocity of individual components
Using Monte Carlo Methods to estimate mass potential parameters on a model describing a galaxy radial velocity in terms of the radius with the bayesian parameter estimation.
Base package to be imported by other members of RvSpectML ecosystem
Better Radial velocities from Stellar Spectroscopy via Machine Learning
Computes Cross Correlation Functions (CCFs) with RvSpectML
Gaia science performance simulation toolkit
Create Your Own Metropolis-Hastings Markov Chain Monte Carlo Algorithm for Bayesian Inference (With Python)
Apply the Nested Sampling Monte Carlo algorithm to fit exoplanet radial velocity data and estimate the posterior distribution of the model parameters
Using the radial velocity method to find the semi-major axis and mass of planets
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