Welcome to BEAT’s documentation!

The Bayesian Evidence Analysis Tool (BEAT) is a Python-based code for measuring and dissecting multi-component emission lines. BEAT was designed to analyze large numbers of spectra (i.e. large surveys or IFU spectroscopy) automatically and determine the number of physical kinematic components in a given set of emission lines. BEAT utilizes the MultiNest sampling algorithm to compute Bayesian statistics and determine the number of components for each fit.

In-depth descriptions of BEAT can be found in the following papers: Fischer et al. 2017 (see the Appendix), Falcone et al. 2024 (see Section 3).

BEAT has been used in numerous peer-reviewed studies. These references include:

• Revalski et al. 2025

• Falcone et al. 2024

• Meena et al. 2023

• Revalski et al. 2021

• Meena et al. 2021

• Trindade Falcão et al. 2021

• Fischer et al. 2019

• Revalski et al. 2018

• Fischer et al. 2018

Please note that the documentation is being written over the spring/summer of 2025. We apologize for any inconvenience!

Contents

• Installation
  • MacOS Systems
  • Simple example fit
• Customizing your fits
  • Reading in data
  • Adjusting parameters
    • target_param
    • cont_instructions
    • fit_instructions
  • Parameters at the bottom of the code block
• Incorporating a broad line fit
  • Method 1: Fit Everything at Once
  • Method 2: Use an Iterative Prodecure
    • Step 1: Fitting only the broad lines
    • Step 2: Fitting the spectrum with new broad parameters
    • Step 3: Fitting the NLR-subtracted spectrum
    • Step 4: Fitting the spectrum with newer broad parameters
• Interpreting the output