"sidecar" overview

The supernova detection pipeline for the Roman Supernova Project Infrastructure Team (SNPIT). This package is designed to performance image difference, detect transient sources, and evaluate efficiency and purity for supernova science with the Roman Wide Field Instrument.

This package is intended to be run within the roman-snpit-env container environment. The instructions below walk you through preparing your environment and running the pipeline step-by-step on Perlmutter at NERSC.

The name "sidecar" (which we could pretend is an acronym for "Supernova Ia DEteCtion AlgoRithm") is following the beverage-theme of Roman Supernova PIT photometry package naming. It also a broader meaning of a supporting package to the main photometry. We need to be able to find supernova in order to do photometry. However, if we get identified targets from a different sources, the sidecar isn't necessary to then do the photometry.

Step 1: Setup Container Environment

This package is compatible with the Roman Supernova PIT environment used to run phrosty:

https://github.com/Roman-Supernova-PIT/phrosty/tree/main/examples/perlmutter

Before using sidecar, first follow the phrosty Perlmutter setup instructions and ensure that you can run phrosty interactively. You can stop following phrosty instructions at the "Running with the Nsight Profiler" section.

Once phrosty is working, return to the same directory where you setup the phrosty environment and proceed with Step 2.

Step 2: Clone sidecar

Run the following in terminal:

git clone https://github.com/Roman-Supernova-PIT/sidecar.git

Once cloning is complete, navigate to the 'sidecar' directory.

Step 3: Detection

To run the detection pipeline, run the following code in sidecar repo of the terminal.

python sidecar/pipeline.py -d [path of the input file] -o [output directory]

E.g.,

python sidecar/pipeline.py -d test/test_one_data_record.csv -o /dia_out_dir

Note the /dia_out_dir only makes sense because we're running in the podman container, where we have bound /dia_out_dir to an output directory.

Can run by specifying the values on the command line

python sidecar/pipeline.py --pointing 53526 --sca 1 --band R062 --template-pointing 5044 --template-sca 8 --template-band R062 -o /dia_out_dir

Can also run by just specifying the pointing, sca, band of the science image

python sidecar/pipeline.py --pointing 53526 --sca 1 --band R062 -o /dia_out_dir

or just by passing the image path

image_path=/dvs_ro/cfs/cdirs/lsst/shared/external/roman-desc-sims/Roman_data/RomanTDS/images/simple_model/R062/53526/Roman_TDS_simple_model_R062_53526_1.fits.gz
python sidecar/pipeline.py --science-path ${image_path} -o /dia_out_dir

sidecar Workflow

• Input: The pipeline takes a csv file with 6 required columns. They will be used as data ids to identify science and template images. During running, the pipeline will loop over each row to perform image difference, source detection, truth retrieval, and truth matching.

  | science_band | science_pointing | science_sca | template_band | template_pointing | template_sca |
  

• Subtraction: Perform image difference using SFFT algorithm.
• Detection: Perform source detection using Source-Extractor.
• Truth Retrieval: Retrieve truth tables of the science image and template image.
• Truth Matching: Match the truth information to the detected sources for evaluating efficiency. Match the detected sources to truth for evaluating purity.
• Evaluation: Evaluate efficiency and purity.