detect_supernova overview

The supernova detection pipeline for the Roman Supernova Project Infrastructure Team (SNPIT). This package is designed to performance image differnece, detect transient sources, and evaluate efficiency and purity of Roman images.

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.

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 detect_supernova, 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 detect_supernova

Run the following in terminal:

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

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

Step 3: Detection

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

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

E.g.,

python detect_supernova/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.

detect_supernova 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.