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SFFT: Saccadic Fast Fourier Transform for image subtraction

.. image:: https://img.shields.io/pypi/v/sfft.svg :target: https://pypi.python.org/pypi/sfft :alt: Latest Version

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.. image:: https://img.shields.io/badge/License-MIT-yellow.svg :target: https://opensource.org/licenses/MIT | Saccadic Fast Fourier Transform (SFFT) is an algorithm for fast & accurate image subtraction in Fourier space. SFFT brings about a remarkable improvement of computational performance of around an order of magnitude compared to other published image subtraction codes.

SFFT method is the transient detection engine for several ongoing time-domain programs, including the DESIRT <https://ui.adsabs.harvard.edu/abs/2022TNSAN.107....1P/abstract>_ survey based on DECam & DESI, the DECam GW-MMADS Survey for GW Follow-ups and the JWST Cycle 3 Archival program AR 5965 <https://www.stsci.edu/jwst/science-execution/program-information?id=5965>. SFFT is also the core engine for the differential photometry pipeline of the Roman Supernova PIT <https://github.com/Roman-Supernova-PIT>.

Get started

• Documentation: https://thomasvrussell.github.io/sfft-doc/ [recommended]
• Installation: https://thomasvrussell.github.io/sfft-doc/installation/
• Tutorials: https://thomasvrussell.github.io/sfft-doc/tutorials/
• Source code: https://github.com/thomasvrussell/sfft
• Contact the author: astroleihu@gmail.com or leihu@sas.upenn.edu

Installation

To install the latest release from PyPI, use pip: ::

pip install sfft

For more detailed instructions, see the install guide <https://thomasvrussell.github.io/sfft-doc/installation/>_ in the docs.

Citing

Image Subtraction in Fourier Space, Lei Hu et al. 2022, The Astrophysical Journal, 936, 157

See ADS Link: https://ui.adsabs.harvard.edu/abs/2022ApJ...936..157H/abstract

Publications using SFFT method

See ADS Library: https://ui.adsabs.harvard.edu/public-libraries/lc4tiTR_T--92f9k0YrRQg