OpticAl Seti Image Subtraction– OasisPy

OASIS is a toolkit for detecting variable objects in astronomical images by means of difference imaging. Includes the OASIS Pipeline, an all-in-one difference imaging utility that takes a set of input images and performs all necessary difference imaging steps on them, outputting a set of source catalogs upon completion. Difference imaging is a notoriously cumbersome task, especially for widely varying data. The OASIS Pipeline was built as a way to largely automate many of the menial processing steps involved in a difference imaging project.

The code is designed to perform quality difference imaging on data that vary widely in pointing, background, seeing, etc. Originally used in processing images of large galaxies, OASIS should work well for both extended objects and simple star fields.

It was developed for use in UC Santa Barbara’s Optical SETI program (project homepage), but can be deployed in any application involving anomaly detection in astronomical data.

The OasisPy package is a set of Python modules that facilitate access to OASIS’s main functionalities.

Features

  • Masking – Masks cosmic rays, hot pixels, saturated stars, CCD defects, etc. Supports the use of weight maps often used in AstrOmatic programs.
  • PSF Modeling – Computes PSF models of all input images using the AstrOmatic software PSFex.
  • Quality Control – Ignores images below a user-defined S/N threshold and/or above a seeing threshold.
  • Registration – Registers images to a chosen reference frame to subpixel precision.
  • Photometric Alignment – Linearly rescales each image’s intensity scale to match that of the reference image.
  • Stacking – Performs a weighted coaddition of the input images to construct a deep, high S/N template image for use in the image subtraction step.
  • Background Matching – Matches the background of the input images to the template image, using an image subtraction method that works well for extended objects with complicated backgrounds.
  • Image Subtraction – Computes a PSF-matching convolution kernel to convolve with the template image, then subtracts the template from the input image. Uses the Optimal Image Subtraction (OIS) algorithm from Christophe Alard (paper).
  • Parameter Optimization – Iterates over a range of OIS parameter configurations looking for the one that yields the best residual image. If a residual fails to meet a certain quality threshold for all parameter configurations, it is masked. Allows for a more robust subtraction that guarantees all residuals in the dataset will be of optimal quality.
  • Source Extraction – Uses the AstrOmatic program SExtractor to extract variable objects from residual frames.
  • Source Filtering – Filters out subtraction artifacts and other phony variable sources.