EMPEROR

Exoplanet Mcmc Parallel tEmpering for Rv Orbit Retrieval

Overview

EMPEROR (Exoplanet Mcmc Parallel tEmpering for Rv Orbit Retrieval), is a Python-based algorithm that automatically searches for signals in Radial Velocity timeseries, employing Markov chains and parallel tempering methods, convergence tests and Bayesian statistics, along with various noise models. A number of posterior sampling routines are available, focused on efficiently searching for signals in highly multi-modal posteriors. The code allows the analysis of multi-instrument and multi-planet data sets and performs model comparisons automatically to return the optimum model that best describes the data.

Make sure to check the documentation!

Why EMPEROR?

• It's really simple to use
• It has a series of configuration commands that will amaze you
• Advanced Noise Model
• Quite Flexible!

Dependencies

This code makes use of:

• Numpy
• Scipy
• pandas

• matplotlib>=3.5.1

• kepler

• reddemcee
• reddcolors
• tabulate
• termcolor
• tqdm

All of them can be easily installed with pip.

For additional capabilities, you can install:

• arviz
• celerite2
• corner
• dynesty
• emcee
• scikit-learn

Installation

Pip

In the console type

pip3 install astroEMPEROR

From Source

In the console type

git clone https://github.com/ReddTea/astroEMPEROR.git

Installation Verification

Download the tests folder and run test_basic.py to make sure everything works!

In terminal:

python test_basic.py

Quick Usage

We need to set up our working directory with two subfolders, datafiles and datalogs, the former for data input, the later for output.

📂working_directory
 ┣ 📜mini_test.py
 ┣ 📂datafiles
 ┃ ┣ 📂51Peg
 ┃ ┃ ┗ 📂RV
 ┃ ┃ ┃ ┗ 📜51peg.vels
 ┣ 📂datalogs
 ┃ ┣ 📂51Peg
 ┃ ┃ ┗ 📂run_1

Running the code is as simple as:

import astroemperor

sim = astroemperor.Simulation()

sim.set_engine('reddemcee')
sim.engine_config['setup'] = [2, 100, 500, 1]
sim.load_data('51Peg')  # read from ./datafiles/

sim.plot_trace['plot'] = False  # deactivate arviz plots
sim.autorun(1, 1)  # (from=1, to=1): just 1 keplerian

Outputs

All results can be found in the datalogs folder. You will see chain plots, posterior plots, histograms, phasefolded curves, the chain sample and more!