EMPEROR

EMPEROR (Exoplanet Mcmc Parallel tEmpering for Rv Orbit Retrieval), is an open-source Python-based framework designed for the efficient detection and characterisation of exoplanets by using radial velocity (RV) methods and astrometry.

Its combination of performance, flexibility, and ease of use makes it a robust tool for any exoplanet detection endeavour. EMPEROR integrates Dynamic Nested Sampling (DNS) and Adaptive Parallel Tempering (APT) Markov Chain Monte Carlo (MCMC), supporting multiple noise models such as Gaussian Processes (GPs) and Moving Averages (MA). The framework facilitates systematic model comparison using statistical metrics, including Bayesian evidence and Bayes Information Criterion (BIC), while providing automated, publish-ready visualisations.

The code is easy to install and easy to use, providing full publication-ready plots and copy-pastable latex tables:

import astroemperor as emp
import numpy as np
np.random.seed(1234)

sim = emp.Simulation()
sim.load_data('51Peg')  # folder read from /datafiles/

sim.engine_config['setup'] = [10, 500, 3000, 1]  # ntemps, nwalkers, nsweeps, nsteps

sim.add_condition(['Period 1', 'limits', [3, 5]])  # short chain

Some Automatic Plots:

Best FitArviz: Trace and MorePosteriorsEstimatesTemperature Ladder

Full model, and phase-folded per planet

For every model block:

CornerTraceHDI IntervalsNormalised Posteriors

For every model block, and temperature:

ScatterHigher temperatures!Different styles 1Different styles 1

For every parameter:

GMEHistograms

Swap AcceptanceLadderDensity