Monte Carlo InfraRed Spectral Energy Distribution (MCIRSED)

A Python tool for fitting the 8--1000 micron dust emission of galaxies published in Drew et al. 2021 in preparation.

Required Packages:

As of the time of writing, the main Python package we use for MCMC fitting, pymc3, will not work with Python 3.8 without a hack to the code. Hopefully this will change in the future. We recommend you follow the instructions here to create a new conda environment with Python 3.7.

• Python 3.7
• latest anaconda stable build
• pymc3
• libpython
• corner
• m2w64-toolchain (if using Windows)

Getting Started:

For an example of how to use the code, run example_data.py to generate data for two galaxies. Next run example_fit_mcirsed.py. There are instructions in the comments of this script that will guide you through working with. We recommend you save a copy of example_fit_mcirsed.py before editing the inputs.

Inputs:

Required inputs to the code are wavelengths of observations in microns, flux densities and uncertainties in mJy, and a redshift.

Parameters that may be free or held fixed:

• Alpha (power law slope)
• Beta (dust emissivity)
• Lambda_0 (wavelength where dust opacity = 1. Referred to in the code as w0)

Outputs:

The code will output a pandas dataframe save as a .pkl file containing, in this order:

• z: redshift
• fixAlphaValue: value alpha was fixed to or None if free parameter
• fixBetaValue: value beta was fixed to or None if free parameter
• fixW0Value: value lambda_0 (W0) was fixed to or None if free parameter
• tune: how many tuning steps the sampling was run with
• MCSamples: number of MC samples
• CMBCorrection: whether CMB was corrected for
• trace_Norm1: norm1 parameter for each MC sample
• trace_Tdust: dust temperature for each MC sample
• trace_alpha: alpha for each MC sample
• trace_beta: beta for each MC sample
• trace_w0: lambda_0 (w0) for each MC sample
• trace_LIR: log LIR for each MC sample
• trace_lPeak: peak wavelength for each MC sample
• median_Norm1: median norm1 across all samples
• median_Tdust: median tdust
• median_alpha: median alpha
• median_beta: median beta
• median_w0: median lambda_0 (w0)
• median_LIR: median log LIR
• median_lPeak: median peak wavelength
• Norm1_16th: 16th percentile for norm1 samples
• Tdust_16th: 16th tdust
• alpha_16th: 16th alpha
• beta_16th: 16th beta
• w0_16th: 16th lambda_0 (w0)
• LIR_16th: 16th log LIR
• lPeak_16th: 16th peak wavelength
• Norm1_84th: 84th percentile for norm1 samples
• Tdust_84th: 84th tdust
• alpha_84th: 84th alpha
• beta_84th: 84th beta
• w0_84th: 84th lambda_0 (w0)
• LIR_84th: 84th log LIR
• lPeak_84th: 84th peak wavelength
• dataWave: wavelengths of data used for each galaxy in microns
• dataFlux: fluxes in mJy used for each galaxy in microns
• dataErr: errors in mJy used for each galaxy in microns