arksia 0.1.1

arksia, part of the ALMA large program ARKS

arksia ('ARKS Image Analysis') - a pipeline for 1D image analysis of the ALMA large program ARKS ('ALMA survey to Resolve exoKuiper belt Substructures').

Dependencies

Use the newest versions of frank and MPoL:

• frank - pip install git+https://github.com/discsim/frank.git
• MPoL - pip install git+https://github.com/MPoL-dev/MPoL.git

Install

pip install arksia

Pipeline scope

The pipeline is run from the terminal using input parameter files. It has the following, modular capabilities:

• extracts a radial brightness profile from a clean image
• processes an existing rave fit to obtain a brightness profile and 1d, 2d residuals in consistent units
• runs frank to obtain a brightness profile, obtain 1d residuals, image the 2d residuals (using MPoL)
• runs frank for 1+1D vertical (disk aspect ratio) inference
• produces plots to compare clean, rave, frank brightness profiles, radial visibility profiles, images, residuals
• produces plots to assess frank vertical inference over grids of h, alpha, wsmooth
• adds utilites to prepare visibility files for the above and to save/load/interface with all of the above
• the pipeline runs from general and source-specific parameter files to do any combination of the above
• the pipeline can be run in bulk (across multiple sources) to perform analysis and summarize results

Prior to running the pipeline for a new source

Before running any pipeline routines:

1. Create the following directory structure:

• Root directory: '[disk name]'
  • Subdirectories: 'clean', 'frank', 'rave'

2. Download and place the following files in these directories:

• root dir: 'MCMC_results.json' (used to read assumed disk geometry and stellar flux) and 'pars_image.json' (contains clean image RMS noise per robust value)
• 'clean' dir: Primary beam-corrected CLEAN image ('.pbcor.fits'), primary beam image ('.pb.fits'), CLEAN model image ('*.model.fits') for each robust value
• 'frank' dir: Visibility datasets ('*.corrected.txt')
• 'rave' dir: Rave fit array files ('*.npy') for each robust value

3. Add the disk to your source parameters (.json) file

• set 'base: SMG_sub', 'clean: npix' and 'clean: pixel_scale' according to the '.fits' filenames (these will be used to determine the filenames of the appropriate images to load)
• set 'rave: pixel_scale' according to the Rave model filename
• set 'base: dist' and 'frank: SED_fstar' according to the github wiki (see 'ARKS sample' there)
• 'frank: custom_fstar' and 'frank: bestfit' will be determined by running frank fits

Running the pipeline for a single source

The main pipeline file is pipeline.py. It can be run from the terminal for fits/analysis of a single source with python -m arksia.pipeline -d '<disk name>', where the disk name is, e.g., 'HD76582'.

By default the pipeline runs using the parameter files ./pars_gen.json (which contains parameters to choose which of the above pipeline modules run, as well as sensible choices for the pipeline parameters applicable to all sources) and ./pars_gen.json (which contains sensible choices for source-specific, best-fit parameters). For a description of the parameters, see description_pars_gen.json and description_pars_source.json.

Setting up frank fits

• To run frank, you will likely want to adjust the alpha, wsmooth and scale_heights parameters in ./pars_gen.json.

• When performing frank fits to find a radial profile, I recommend setting method to "LogNormal" to perform fits in logarithmic brightness space. Not all parts of the pipeline support linear brightness space fits with enforced non-negativity; this is because the logarithmic fits are in general a better choice. The exception is that when running a frank 1+1D fit to find h, method must be "Normal" (it will be enforced).

Running the pipeline for multiple/all sources

The pipeline can be looped over multiple sources using bulk_pipeline_run.py via python bulk_pipeline_run.py (you may want to adjust the referenced .json parameter files there).

Obtaining key results for multiple/all sources

Survey-wide results are a .txt file per source with all radial brightness profiles (clean, rave, frank) sampled at the same radii, and figures with a panel for each source showing the clean, rave, frank brightness profiles (one figure without uncertainties, one figure with). These are generated with bulk_pipeline_results.py via python bulk_pipeline_results.py (you may want to adjust the referenced .json parameter files there).