Short Fourier Transforms for Fresnel-weighted Template Summation.
Implementation of gravitational-wave data-analysis tools described in Tenorio & Gerosa (2025) to operate using Short Fourier Transforms (SFTs).
See the examples for a quick-start on using SFTs and iphenot (/ˈaɪv ˈnɒt/).
sfts contains two main modules:
- kernels.py: Fresnel and Dirichlet kernels to compute scalar products using SFTs.
- iphenot.py: jaxified re-implementation of the
inspiral part of the
IMRPhenomTwaveform approximant.
The SFT convention in this package is compatible with that in the
LVK
69D1
.sft file format.
Checkout fasttracks'
search example
to learn about reading .sft files into jax arrays.
sfts can be pulled in from PyPI:
$ pip install sfts
To pull in jax's GPU capabilities, use:
$ pip install sfts[cuda]
Alternatively, this repository itself is pip-installable.
If the tools provided by sfts were useful to you, we would appreciate a citation of
the accompanying paper:
@article{Tenorio:2025gci,
author = "Tenorio, Rodrigo and Gerosa, Davide",
title = "{Scalable data-analysis framework for long-duration gravitational waves from compact binaries using short Fourier transforms}",
eprint = "2502.11823",
archivePrefix = "arXiv",
primaryClass = "gr-qc",
doi = "10.1103/PhysRevD.111.104044",
journal = "Phys. Rev. D",
volume = "111",
number = "10",
pages = "104044",
year = "2025"
}
Whenever applicable, please consider also citing the IMRPhenomT papers listed here.